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🚨Standard Chartered CISO's Approach to Designing Judgment Out of Security

Ashish Rajan — Wed, 02 Sep 2026 20:53:58 +0000

Hello from the Cloud-verse!

This week's Cloud Security Newsletter topic: Design the Judgment Out of Your Security Controls (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter!

Eight stories cleared the bar this week and none of them was a headline breach in the traditional sense. What they share is smaller and more uncomfortable: in almost every case, the thing the attacker needed was already sitting somewhere the security model had not accounted for. JFrog instances without an explicitly configured join key were handed a "phantom" one that attackers could forge against. Manchester Airports Group's customer-engagement API credentials were readable in the page source. METR's model-provider API key was on a researcher's personal EC2 box behind authentication that failed open, and nobody noticed three weeks of theft because the credits were free and free resources have no billing alarm.

Against that, this week's conversation is with Cezary Piekarski, Group Chief Information Security Officer at Standard Chartered Group, hosted by Ashish Rajan. Cezary runs security for an institution operating across 50-plus markets, and his position is that detection-led security stopped being viable at that scale years before AI entered the conversation. His prescription is blunt: eliminate the corner cases that require a human to make a call, because "judgment tends to be wrong."[Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 JFrog Artifactory CVE-2026-82329: admin tokens minted on default configs, exploited three days after patch. Go to 7.161.20 and rotate every token it issued.
🚨 Both PaperCut zero-days in CISA KEV, federal deadline September 14. The first patch was bypassed within a day, so check the build, not the ticket.
21,899 Exchange servers still exposed to a capture-replay auth bypass. Exploit code is public and MFA is not in the path.
McKesson and Manchester Airports Group: both lost data through the SaaS tier, one by vishing into Salesforce and Snowflake, one by an API key in the page source.
Cezary Piekarski's control test: if a control needs someone to judge whether an event is benign, it is the wrong control.

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. CISA adds both PaperCut zero-days to KEV as the vendor's first patch gets broken

Primary source: CISA KEV alert
Reporting: BleepingComputer, SecurityWeek
Analysis: Huntress, Rapid7, PaperCut advisory

What Happened

CISA added CVE-2026-81578 (an authentication bypass letting unauthenticated requests reach administrative configuration functions) and CVE-2026-82078 (unsafe dynamic class loading in the database connection utilities) to the KEV catalog on August 31, with a federal remediation deadline of September 14. PaperCut published its bulletin on August 27 and shipped a first emergency patch for NG/MF versions 25 and 26 on August 28. watchTowr found multiple bypasses of that fix plus an additional authentication bypass, and PaperCut released a second emergency patch later the same day. Huntress saw the first exploitation attempts on August 26 and has confirmed attacks against at least two customers. As of August 30 the vendor advisory still described an official combined release as in progress.

Why It Matters

The vendor's fix was defeated inside a day, which means the remediation status in your vulnerability management system is unreliable for anyone who patched on August 28 and provisional for everyone else until the official build lands. Print management is also a category most cloud security programs file under facilities IT, so PaperCut frequently sits outside the asset inventory that drives a KEV-triggered emergency change window. One observed actor is abusing the auth bypass to hijack PaperCut's external user-lookup and dump database tables through Derby, which converts a print server into a directory data source. Roughly 1,000 instances are internet-exposed.

Action for defenders: Check the running build rather than the ticket, re-read the vendor advisory for the official combined release, and replay the published IoCs against logs going back to August 26.

2. JFrog Artifactory auth bypass weaponized to mint admin tokens three days after patch

Primary source: The Hacker News
Reporting: SecurityWeek, The Register
Analysis: Dark Reading

What Happened

CVE-2026-82329 (CVSS 9.8) is an authentication weakness in JFrog Access, the component that issues and validates Artifactory credentials. watchTowr's finding is that instances without an additional join key configured receive a "phantom" join key attackers can forge against to mint administrator-level credentials, with no authentication and no user interaction needed under default configuration. JFrog patched in Artifactory 7.161.20 on August 28. watchTowr's Yordan Ganchev reported weaponization beginning September 1, used to generate admin tokens and enumerate users, groups, credential sets, and federated access topologies. Self-managed deployments are affected; the JFrog SaaS platform is not.

Why It Matters

The vulnerable state is the default state, so the exposed population is every self-hosted instance nobody deliberately hardened. What the attackers do first is telling: they enumerate the federated access topology, mapping which other systems trust this registry before touching anything. Artifactory admin sits upstream of build pipelines and production artifacts, so the blast radius is whatever your deployment automation will pull.

Action for defenders: Patch internet-exposed self-managed Artifactory to 7.161.20 today, then treat every access token, federated trust, and service credential that instance ever issued as suspect and rotate. Restoring the pre-patch credential state restores the attacker's access with it.

🛠 If you only do one thing this week: Pull the list of every non-human credential your artifact registry, CI system, and AI orchestration hosts have issued in the last 90 days, and check which of them have an expiry, a scope, and a spend or rate ceiling attached. Three of this week's eight stories turn on a credential that had none of the three.

☁️ 3. Langflow RCE exploited to harvest cloud credentials from the AI application layer

Primary source: SecurityWeek
Reporting: BleepingComputer, Dark Reading
Analysis: ZDI advisory ZDI-26-034

What Happened
VulnCheck reported active exploitation of CVE-2026-0768 (CVSS 9.8), an unauthenticated RCE in the code validator of Langflow's custom component editor. A user-supplied string reaches Python execution without validation, giving arbitrary code execution as root. The flaw went through ZDI in July 2025 and was publicly disclosed as a zero-day in January 2026; every release through 1.4.2 is affected. VulnCheck observed the current wave from August 29 — environment-variable queries, secret-key reads, and SSH access attempts, mostly originating from Russia. Before 2026, one Langflow vulnerability was known to be exploited in the wild. Eleven more have been targeted since.

Why It Matters
Nothing here is a payload drop. The post-exploitation activity is a credential sweep: Langflow's secret_key, environment variables holding API keys and cloud credentials, SSH keys, .env files. An AI orchestration host is an aggregation point for exactly the secrets that grant access to everything downstream of it, and it usually gets stood up by a platform or data team without passing through the application onboarding process that would have caught the exposure.

Action for defenders
Inventory Langflow by network exposure rather than by ownership record, confirm version against 1.4.2, and rotate any cloud or model-provider credential that has ever been present in a Langflow environment.

🏥 4. Roughly 21,900 Exchange servers still exposed to a mailbox-takeover auth bypass with public exploit code

Primary source: BleepingComputer
Reporting: NCSC-NL advisory
Analysis: Microsoft MSRC, Shadowserver dashboard

What Happened

CVE-2026-62911, reported by DEVCORE's Orange Tsai and patched in the August 2026 Patch Tuesday, is an authentication bypass by capture-replay affecting Exchange Server 2016, 2019, and Subscription Edition. Microsoft's advisory states an attacker "would be able to take over the mailboxes of all Exchange users." Shadowserver counted 21,899 IP addresses with an unpatched Exchange fingerprint on September 1, concentrated in the United States (6,200) and Germany (5,100). Germany's BSI put the figure at roughly 85% of German on-premises Exchange servers. NCSC-NL reports exploit code is already circulating. Microsoft has not confirmed in-the-wild exploitation.

Why It Matters

The attack replays captured authentication traffic, so every control your program built around credential strength sits outside the path being abused.

Action for defenders
Query external attack surface data for Exchange fingerprints you did not know you owned, confirm the August update is present, and put a hard date against the October 2026 end of the Extended Security Updates program for any 2016 or 2019 server still standing.

🛡️ 5. Anthropic warns that stolen Claude session cookies are bypassing MFA and draining accounts

Primary source: SecurityWeek
Reporting: BleepingComputer, Security Affairs

What Happened

Anthropic emailed affected Claude customers to warn that commodity infostealer malware on their own machines had let attackers hijack authenticated sessions and consume usage. The named families are Vidar, Lumma, StealC, RedLine, and Acreed on Windows, plus Atomic Stealer on a smaller number of macOS devices. Anthropic assesses that a threat actor was selecting Claude sessions out of broader harvested infostealer data. The company signed out compromised sessions, removed saved payment methods so accounts could not be charged, and refunded charges it identified as unauthorized. The signal it gave users was a usage limit that refills and then drains while the owner is not using the product.

Why It Matters

The stolen artifact is a post-authentication session cookie, which means multi-factor authentication is not in the attack path and neither is any conditional access policy that evaluates only at sign-in. The detection signal Anthropic handed users was consumption anomaly, and that is not a signal most enterprise SaaS monitoring collects for AI tools. Any organization where employees reach AI services from unmanaged endpoints carries this exposure with no telemetry to see it.

Action for defenders:
Confirm your AI service subscriptions are enrolled in SSO with short session lifetimes rather than long-lived browser sessions, and add per-account consumption baselines to your SaaS anomaly detection.

6. METR discloses a stolen API key, three weeks of undetected use, and roughly $600,000 in model credits burned

Primary source: METR security update
Reporting: The Register, The Hacker News
Analysis: Infosecurity Magazine

What happened: METR published a post-incident writeup covering two 2026 incidents. In March, a researcher ran agents on a personal EC2 instance made public behind Google authentication; the application held an API key for METR's public-models account, and a fail-open flaw silently disabled authentication, leaving the system exposed for several days. METR assesses the attacker found the instance by mining certificate transparency logs for recently registered sites carrying LLM and agent keywords. The attacker prompted the agent to reveal its API key, added an SSH key for persistence, and consumed credits worth approximately $600,000 over three weeks. In May, attackers systematically probed METR's public infrastructure using agents to automate discovery, and a read-only SQL query mechanism exposed through the public transcript viewer carried a bug that could have reached unpublished evaluation data. An independent researcher disclosed it and was paid a bounty.

Why it matters: Three failure modes here generalize cleanly. Certificate transparency logs are now a targeting feed for finding freshly deployed AI applications, which kills the assumption that a new internal tool is obscure. A fail-open authentication default in an agent-built application produces a silent public exposure rather than a visible error. And the reason nobody caught three weeks of theft is that the credits were free, so there was no spend ceiling and no billing signal to trip. The detection depended on a control that did not exist because the resource had no price. METR's remediation was architectural: an isolated public production environment separated from internal infrastructure.

Action for defenders: Enumerate model-provider API keys living outside your managed infrastructure, and check that every key you issue carries a spend or rate ceiling — including keys covered by free or granted credits.

7. McKesson confirms exfiltration as ShinyHunters claims 284 million records and a $55M demand

Primary source: SecurityWeek
Reporting: BleepingComputer, Help Net Security
Analysis: McKesson SEC filing, McKesson cybersecurity notice

What happened: McKesson, which distributes roughly a third of prescription medicines to North American hospitals and pharmacies, disclosed in an SEC filing that it discovered a cybersecurity incident on August 25. It later confirmed data was exfiltrated from a subset of customers in its Oncology & Multispecialty and Medical-Surgical business units, said the incident involved third-party applications, and stated it did not disconnect systems in response. ShinyHunters added McKesson to its leak site around the same time, claiming 284 million records and demanding approximately $55 million with a September 1 negotiation deadline. The group told BleepingComputer it gained access through voice phishing against multiple McKesson employees, then moved into Salesforce and Snowflake and pulled roughly a terabyte over four days. Every figure and the access path are attacker claims; McKesson has confirmed exfiltration but not scope, count, or attribution, and BleepingComputer reports ShinyHunters clarified that 284 million is a raw record count rather than unique individuals.

Why it matters: If the attacker's account holds, no software vulnerability appears anywhere in the chain. A phone call produced credentials, and those credentials were sufficient for bulk export from two separate SaaS and warehouse platforms. That makes the controlling variable your query and export limits on the analytics tier. A terabyte over four days is a volume that data-warehouse egress monitoring can catch and mailbox-centric SaaS monitoring cannot.

Action for defenders: Check that your Snowflake and Salesforce tenants enforce network policies and export volume thresholds on human user sessions rather than only on service accounts, and confirm helpdesk identity verification covers voice-channel credential and MFA reset requests.

8. Manchester Airports Group breach traced to SaaS API credentials exposed in client-side JavaScript

Primary source: BleepingComputer
Reporting: MAG disclosure coverage, SecurityWeek
Analysis: Security Affairs

What happened: Manchester Airports Group, the UK's largest airport operator, disclosed on August 27 that a third party stole customer data tied to car park, lounge, and Fast Track bookings and in-airport Wi-Fi registrations at Manchester, London Stansted, and East Midlands. The extortion group FulcrumSec claimed responsibility, told BleepingComputer it took approximately 86 GB including a 21.5 GB Manchester customer export, and said it obtained access using airport-specific Iterable API credentials exposed in client-side JavaScript. BleepingComputer validated one traveller's record against known purchase history and found the sampled data ran broader than MAG's initial disclosure, covering booking references, prices, parking dates and times, historical spending, IP addresses, approximate locations, and device information. No payment card data appeared in the samples. MAG declined to address the specific claims. the volume, the record counts, and the access path are FulcrumSec claims; the ~8.7 million customer figure comes from a MAG spokesperson quoted in secondary reporting.

Why it matters: A customer-engagement platform API key shipped to the browser is a class of exposure no cloud posture tool inspects, because the credential is not in your cloud account. It is in your front-end bundle. The data combination matters more than the headline count: a full UK postcode narrows to roughly 15 addresses, and pairing that with vehicle registration, terminal, and a confirmed future travel date produces a phishing pretext the target can verify as genuine.

Action for defenders: Grep your production front-end bundles for third-party SaaS API keys and tokens, and confirm every marketing or engagement platform integration runs through a server-side proxy with scoped, rotatable credentials.

🎯 Cloud Security Topic of the Week:

Prompt filtering is stack protection, and we already know how that ended

Cezary Piekarski's central argument this week is a historical one. He is old enough to remember "smashing the stack for fun, fun and profit" and the buffer overflow era that followed, and he watched the industry spend years trying to protect the stack and filter input so a program could not overwrite the memory segments steering execution.

"And this attempt was actually futile in majority of the programming languages. And we kept improving those mechanisms and we keep- kept failing." — Cezary Piekarski

What ended the buffer overflow era was not a better filter.

"the problem faded away when we moved towards a slightly different approach to the way we design CPUs and we, we introduced the memory management in the CPU" — Cezary Piekarski

His claim is that LLM security is currently at the stack-protection stage of the same curve. The industry is investing in classifiers, in input sanitization, and in artificially separating the instruction channel from the data channel inside a prompt.

"There is lots of efforts to think about how you artificially try to distinguish the instruction piece from the data piece in prompt, but I also see all of those attempts as largely futile at this stage." — Cezary Piekarski

"it, it might be that there is something much more deeper into the way LLMs operate that we're required to change for them to be secure." — Cezary Piekarski

For a practitioner, the useful consequence is not fatalism about prompt filters. It is a budgeting decision. If input filtering is a holding action rather than a solution, then the controls worth over-investing in are the ones that limit what a compromised model context can reach: scoped credentials, egress restrictions, and the blast radius around the agent rather than the prompt going into it. Cezary's own framing of the unknown-unknowns problem makes the same point from the governance side.

"you need to be very clear about the limitations of your controls, but also about the limitations that you might not be cognizant of. So, so those are unknown unknowns in the process." — Cezary Piekarski[Listen to the full episode →]

Featured Experts This Week 🎤

Cezary Piekarski — Group Chief Information Security Officer, Standard Chartered Group.
Ashish Rajan - CISO | Co-Host, AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Security as a business enabler — a cliché unless backed by two things: an engagement and operating model that embeds security people into business or engineering structures, and a security culture built on priorities shared with the business.
Time to adapt — a measure of "your malleability of the organization," how fast the organization can pivot. Cezary cites it as an existing measurable that programs already talk about.
Deception (as a control) — traps and breadcrumbs placed in the environment. Only effective when designed into infrastructure and applications so it is "believable, to be genuine," and adaptable to specific adversary TTPs.
UEBA — behavioral detection built on the assumption that unusual activity is malicious and usual activity is benign. Cezary treats that assumption as the technology's core defect.
Training data poisoning — attacks that "leverage training data poisoning to influence the output of LLMs." Cezary expects brand infringement and social engineering categories to emerge from it.
Phantom join key (from this week's news, not the episode) — in JFrog Access, a join key automatically assigned to instances with no additional join key configured, which attackers can forge against to mint admin credentials.
Capture-replay authentication bypass (from this week's news) — the CVE-2026-62911 technique: capture authentication traffic, replay it, impersonate the user. Credential strength controls are not in the path.
Fail-open authentication (from this week's news) — an authentication layer that grants access when it errors rather than denying it. The root cause of METR's March exposure.

This week's issue is sponsored by Varonis

What’s the blast radius of your AI agent?

Your cloud provider may secure the AI platform but you still decide what your agents can access, what permissions they have, and what actions they can take.

On September 2, I’m hosting an online panel with the security research team at Varonis on to break down real AI agent attack scenarios and what security teams should be doing about them.

Join us!

💡Our Insights from this Practitioner 🔍

1. "Business enabler" stays a cliché until the operating model changes

Cezary opens by refusing the phrase on its own terms.

"Yeah, it is, it is probably, like, the most, most overused cliche phrase in, in the security industry. And, and I think it re- it remains, uh, a cliche, uh, until, until you do at least two important things, and those two important things are: the first one is is a proper engagement and operating model" — Cezary Piekarski

The two things are structural, not attitudinal. First, an engagement model that physically places security people inside the business: a security person embedded in the engineering squad, connected upward through a chapter or equivalent construct, or a BISO organization mapped to businesses and geographies in a more traditionally structured company. Second, a culture built on shared priorities.

"So as far from being a department of no as, as possible." — Cezary Piekarski

He is careful not to make the culture point mushy. Roles differ, disagreements happen, and he notes they "can get heated if people are very passionate about their jobs." The shared goal is what makes the disagreement productive rather than terminal.

2. Detection-first security stopped scaling before AI arrived

Asked whether AI changes the traditional reactive detection-and-response model, Cezary's answer is that the change already happened and AI is only accelerating it.

"when you really think about the scale, if you operate at y- 50 plus markets and you have relatively extensive technology footprint, it means that you probably ingest a tune of 50 terabytes of observable data a day plus, right?" — Cezary Piekarski

"which means that the manual triage and the very detective focused, uh, approach is, is not right for you no matter whether you have AI or, or not, right?" — Cezary Piekarski

The prescriptive half of this is the most transferable line in the episode.

"obviously eliminate this type of corner cases because this, uh, includes jud- judgment, and judgment tends to be wrong. So try to make it as, as preventative, as defined, as binary within the guardrails as possible." — Cezary Piekarski

Read that as a test you can apply to any control in your stack. If it requires a person to decide whether an event is benign, it is a weaker control than one that makes the event impossible. He extends the same logic to response: there is "not really much alternative to having a fully automated, uh, response and containment capabilities," and the organizational work is defining the boundaries within which a machine may contain, including the business trade-offs, because "some of those decisions are not, uh, cost-free."

3. UEBA's premise is wrong, and the fix is in the application

Cezary names the failure precisely.

"what usually happens is that people fall into the trap of unusual means means malicious, usual means benign, right?" — Cezary Piekarski

Ashish pressed him on whether humans are patterned enough for the technique to work at all, with an analogy that will land with anyone who has tuned one of these systems.

"I may be drinking the same coffee in the same cafe for, I don't know, three months in a row, but one day I just got over the coffee and I decided to go to another shop." — Ashish Rajan

Cezary agrees the human-pattern critique is real, and adds a structural one:

"when you have the obser- ob- observation window that is wide enough, everything becomes the same color, right?" — Cezary Piekarski

But he treats both as secondary to a practical problem: the systems covered by behavioral tooling frequently do not have the telemetry to support the conclusion, or the telemetry is too generic to draw one. His alternative is to fix the application rather than instrument around it.

"it's, it's better to do the right segregation of duties within the application implement a proper authentication authorization model, uh, and ensure the application is just well designed to be secure instead of trying to make it deliberately unsecure and then overlay some sort of behavioral detection engine on top of it to try to detect outliers." — Cezary Piekarski

He is explicit that AI does not rescue the technique. It "is only em- emphasizing and reinforcing th- this problem at the, at the machine scale."

4. Deception is due a revival, and agentic attackers are the reason

Cezary is dismissive of how deception has historically been bought: after the security program is built, with surplus budget, breadcrumbs scattered and hope applied. Done that way it sometimes works, and he says so. Done properly it requires two design conditions. Deception has to be built into the infrastructure and applications so it reads as genuine, and the platform has to pivot to the TTPs actually in use.

"there might be a revival of deception platforms but clearly they need to be done in a very different way. They need to be designed into the ecosystem to be believable, and they need to be flexible enough to adjust to the TTPs and all specific campaigns that you are seeing out there." — Cezary Piekarski

His reason for expecting the revival is the interesting part, and it is a genuinely useful asymmetry:

"we are already seeing that some of the agentic attack scenarios broadly understood, uh, they have a tendency of walking into this trap, right?" — Cezary Piekarski

"Because they don't have this, this instinct of, uh, of, of experienced red teamer or an attacker, which is like something fishy here, right?" — Cezary Piekarski

An experienced human operator gets suspicious when access comes too easily. An agent optimizing toward a goal does not.

5. AI is already reliable on known vulnerability classes

Cezary declines to treat this as an open question.

"It's, uh, empirically confirmed that, that frontier models or, and not frontier models are, uh, are robust in identifying known classes of, uh, of vulnerabilities." — Cezary Piekarski

The example he reaches for is the one that should worry anyone running a patch cadence:

"situations in which you have a sort of be- before patch and post-patch binary, and you diff those two and, and you derive vulnerability that was addressed by patch." — Cezary Piekarski

Patch-diffing is not new. What is new is the cost of doing it, and the number of people who can now do it without the reverse engineering background that used to gate the technique.

6. Prompt filtering is stack protection, repeating

Covered above as the Topic of the Week. The compressed version: the buffer overflow class did not fall to better input filtering, it fell to an architectural change in how CPUs handle memory, and Cezary expects the LLM equivalent to require a similarly deep change rather than a better classifier. Ashish put the same uncertainty from the defender's side.

"We have a category called prompt injection, but we don't know if we have seen all versions of it." — Ashish Rajan

7. The under-discussed attack is the one everyone is discussing

Asked which AI attacks deserve more attention, Cezary picks prompt injection, the one with the most attention already, because the attention rests on a false premise.

"there is this, I think a certain naiveté in a f- in, in, in the way people think about it is that, that by creating a better and better filters you can, you can protect, uh, the model from, uh, from this category of attacks. I, I don't think that this is actually what happens." — Cezary Piekarski

He points to format-shifted variants that walk past filters: encoding tricks beyond ASCII and base64, and images used as an injection channel. The structural reason he expects filters to keep losing is the breadth of the input surface, "the richness of the communication channels that are available to interact with, uh, with, uh, large language models and with the harnesses."

His second answer has a longer fuse.

"I think that there will be lots of data poisoning attacks especially when, when more established LLMs will be out there, and you're gonna see see categories of attacks in this space related to brand infringement to social engineering and to other broader categories of attacks that will leverage training data poisoning to influence the, the output of LLMs." — Cezary Piekarski

8. Board communication is a three-part sequence, and most CISOs run only the third part

Cezary has been briefing boards for years, first in consulting and then in industry roles, and his model is sequential.

"you need to use a mix of generic education that is not really risk-focused. It's more about simply how technology works." — Cezary Piekarski

Step one is education with no risk framing attached, deliberately staged with dissent in the room: "diverse points of view being brought to the table with people who disagree with you also speaking." Step two is a small one-to-one space for the follow-up questions people will not ask in a committee. Only then does risk enter.

"And only when you have those two in place you can talk about risk." — Cezary Piekarski

And the failure mode, which most readers will recognize:

"there is a CISO coming to the board and saying, "Oh, you know, the number of incidents this, and the number of incidents that, and buffer overflow here and other buffer overflow there," right? It never works because people don't have the taxonomy and context but also they simply don't have the wealth of diverse perspectives that help them to judge whether, your SLA for vulnerability remediation makes sense or not." — Cezary Piekarski

9. Security data versus privacy is a "fake dilemma"

Ashish raised the tension he keeps hitting with US and UK financial institutions: AI wants more data, privacy obligations want less. Cezary rejects the framing.

"I think to some extent this is fake dilemma." — Cezary Piekarski

His reasoning is that security work does not actually depend on the personal data:

"what is really important from the security point of view is, is rather the, the metadata the surroundings, the observability that you can bring to understand what is happening rather than the specifics of the individuals, uh, or groups that, that you protect." — Cezary Piekarski

He has made genuine scope-of-oversight trade-offs in prior roles and does not pretend the conflict never arises. He calls it "a very rare situation and very rare problem," and says that in the significant majority of cases the security requirement and the privacy obligation can both be met.

Practical takeaways

Apply Cezary's judgment test to your top ten controls this quarter. For each, ask whether it requires a human to decide if an event is benign. The ones that do are candidates for redesign toward a binary guardrail.
If you run behavioral detection, audit the telemetry underneath it before tuning the model. Generic telemetry produces confident conclusions from insufficient evidence.
Treat scoped credentials and egress limits around AI agents as the durable investment, and input filtering as a holding action with a shelf life.
Before your next board session, count how much of your last three presentations was risk metrics and how much was technology education with no risk framing attached.

CISA Known Exploited Vulnerabilities Catalog — authoritative list of what is being exploited
CISA KEV alert, August 31 2026 — both PaperCut CVEs, September 14 federal deadline
PaperCut security bulletin, 27 August 2026 — vendor advisory and IoCs
Microsoft MSRC advisory for CVE-2026-62911 — Exchange capture-replay authentication bypass
ZDI advisory ZDI-26-034 — Langflow CVE-2026-0768
Shadowserver dashboard — live exposure counts for unpatched Exchange
Update on Security at METR — the fail-open EC2 exposure, certificate transparency targeting, and why free credits removed the detection signal
Huntress: PaperCut actively exploited — attack chain reproduction and observed activity
Rapid7: PaperCut NG/MF critical zero-day exploited in the wild — emergent threat report
McKesson customer cybersecurity information center — the company's own running disclosure

Podcast Episode

AI Security Podcast — Full Episode with Cezary Piekarski

Question for you? (Reply to this email)

🤔 Which of your controls still needs a human to decide whether an event is benign, and what would it take to make that one binary?

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🚨 Talos Catches Agentic AI Inside Real Intrusions: Damien Lewke on Why Hunt First Beats Alert Triage

Ashish Rajan — Wed, 26 Aug 2026 21:30:08 +0000

Hello from the Cloud-verse!

This week's Cloud Security Newsletter topic: Hunt First — Telemetry Over Alerts, and How to Detect AI Agents by Their Tempo (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter!

Ashish sat down with Damien Lewke, founder and CEO of Nebulock and a second-time guest, whose career runs from building the DoD's threat hunting team for a large weapon system through CrowdStrike, Palo Alto Networks, MIT, and Arctic Wolf, where he ran the AI detections product teams behind a 1,200-person SOC. His argument lands in a week that kept proving it: AI for security has over-indexed on closing tickets, while the intrusions that matter are assembled from signals nobody alerted on. The same week, Cisco Talos published evidence that at least one threat actor now runs agentic AI inside its post-compromise operations.[Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 CISA gave federal agencies 72 hours on CVE-2026-21962 (Oracle WebLogic/HTTP Server, CVSS 10.0) and three days on the Gitea RCE (CVE-2026-60004) both under active exploitation; patch and hunt now, whatever your sector
Cisco Talos documented UAT-10147 integrating agentic AI into post-compromise operations detection baselines built for human-speed attackers are now the wrong baselines
Mirage2FA's adversary-in-the-middle kit is linked to potential compromise of ~4,500 Microsoft 365 accounts; OTP-based MFA doesn't stop it move privileged roles to FIDO2/passkeys
GitLab's CVE-2026-19478 went from public advisory to in-the-wild exploitation in about three days, with no leaked PoC needed treat self-hosted DevOps platforms as patch-same-week assets
Lewke's first AI use case for any team: a shadow AI hunt — baseline MCP process executions and child processes, then investigate the deviations

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. CISA gives agencies 72 hours on a CVSS 10.0 Oracle WebLogic flaw patched in January

Primary source: CISA KEV alert
Reporting: SecurityWeek · The Hacker News

What Happened

CISA added CVE-2026-21962, a maximum-severity flaw in Oracle HTTP Server and the WebLogic Server Proxy Plug-in, to the KEV catalog on August 24 with a remediation deadline of August 27 — a 72-hour turnaround instead of the usual three weeks. The flaw allows an unauthenticated attacker with HTTP access to read or modify critical data. Oracle patched it in the January 2026 CPU; reporting ties exploitation to a China-linked actor delivering the SNOWLIGHT downloader across more than 100 countries

Why It Matters

A seven-month-old patch generating a three-day federal deadline tells you the exploitation is current and moving. WebLogic and Oracle HTTP Server front ERP and financial estates that lifted-and-shifted into IaaS and rarely appear in cloud-native vulnerability scans, so KEV processes scoped to internet-facing perimeter assets will miss proxy plug-ins on VPC-internal tiers still reachable through load balancers.

Action for defenders: Inventory Oracle HTTP Server and WebLogic Proxy Plug-in instances across all cloud accounts, including tiers behind ALBs, and confirm the January CPU is applied.

2. Gitea RCE added to KEV: open registration turns "write access required" into "anyone"

Primary source: CISA KEV alert
Reporting: Help Net Security · BleepingComputer

What Happened

CISA added CVE-2026-60004 (CVSS 9.8), a code injection flaw in the self-hosted Git service Gitea, to the KEV catalog on August 25 with a federal deadline of August 28. The diffpatch endpoint can be abused to install and execute a Git hook from repository-controlled content, giving an attacker with ordinary write access shell execution as the Gitea OS user. Versions from 1.17 are affected; 1.27.1 patches it, and at least one operator has reported a cryptominer-style dropper delivered through the flaw.

Why It Matters

On instances with default open registration, "write access required" means anyone who registers an account and creates a repository.

Action for defenders: Upgrade to Gitea 1.27.1, disable open registration where it isn't needed, and review recently created accounts, repositories, and Git hooks for anything unrecognized.

🛠 If you only do one thing this week: Pull your self-hosted developer infrastructure list (GitLab, Gitea, anything with a diffable open-source patch stream) and re-baseline its patch SLA to same-week. Then take Lewke's 30-minute starter: baseline MCP process executions in your environment and list every AI agent you didn't know was running. The week's exploit timelines and the episode's thesis point at the same two gaps.

☁️ 3. GitLab GraphQL flaw weaponized from patch-diff to honeypot detections in about three days

Primary source: The Hacker News
Analysis: Horizon3

What Happened
GitLab shipped an emergency patch for CVE-2026-19478, a critical code injection flaw reachable through GraphQL, on August 17. Researchers demonstrated the bug could be reproduced using only the public advisory and the patch diff, and roughly two days later, on August 20, exploitation attempts appeared against honeypot instances. GitLab has warned customers of active exploitation.

Why It Matters
No leaked PoC was needed; the vendor's own advisory and code changes were the exploit development kit. That collapses the realistic patch window for self-hosted DevOps platforms to the time it takes an attacker to read a diff, which for this bug was under 72 hours. Lewke's framing on the episode was blunter: on the attacker side, the time to exploit is shrinking from months to weeks to minutes, and this is what that looks like on a calendar.

Action for defenders
Confirm self-managed GitLab is on the August 17 emergency release or later and check GraphQL logs from that date forward for anomalous mutations. If you can't patch same-week, put network-layer authentication in front of self-hosted GitLab.

🏥 4. Talos: Chinese-speaking actor UAT-10147 integrates agentic AI into post-compromise operations

Primary source: Cisco Talos — agentic AI report
Analysis: Cisco Talos — SPECTRE implant analysis

What Happened

Cisco Talos published paired reports on UAT-10147, a Chinese-speaking actor compromising internet-exposed Windows and Linux web servers across government, education, media, technology, and gaming sectors. The actor deploys SPECTRE, a cross-platform implant whose Windows build supports 45 commands including credential theft, token impersonation, and BYOVD attacks using vulnerable MSI and Dell drivers, plus a Linux rootkit and BadIIS SEO-fraud tooling. Recovered source code showed AI-driven tooling integrated into reconnaissance, exploitation, payload generation, validation, and persistence, including AI-generated operational playbooks.

Why It Matters

This moves attacker AI from the lure into the operations loop, and it is the strongest public confirmation yet of the premise Lewke builds on in this week's episode: the adversary is increasingly agentic, and its tell is tempo. Kernel-level EDR bypass via signed vulnerable drivers also means the endpoint agent can be blinded, so server-side and identity-plane telemetry become the durable signal.

Action for defenders
Pull the Talos IOCs, hunt for BYOVD driver loads and IIS module tampering on internet-facing web servers, and confirm your EDR blocks known-vulnerable driver hashes rather than only flagging them.

🛡️ 5. Mirage2FA phishing service linked to ~4,500 potentially compromised Microsoft 365 accounts

Primary source: The Hacker News
Analysis: ANY.RUN research

What Happened

Researchers detailed Mirage2FA, a commercial phishing-as-a-service kit running adversary-in-the-middle attacks against Microsoft 365 logins since 2024. Victims are funneled through HTML, XHTML, and SVG attachments to a proxied fake Microsoft login page that relays credentials and one-time 2FA codes to Microsoft in real time and captures the authenticated session cookie. The campaign reportedly reached 9,426 unique addresses with roughly 48% potentially compromised: about 4,532 accounts across 3,518 organizations, concentrated in the US

Why It Matters

The stolen artifact is the session cookie, so OTP-based MFA is bypassed by design and the blast radius extends to every SSO-connected service behind the M365 identity. Victim concentration among technology firms and MSSPs compounds it: one relayed session at a service provider is a foothold into downstream tenants.

Action for defenders:
Move admin and high-privilege M365 roles to FIDO2/passkeys, enable token protection and risky-session revocation, and block or sandbox HTML/XHTML/SVG attachments at the mail gateway.

6. SAML signature-confusion chain in miniOrange SSO plugin exploited for admin logins

Primary source: BleepingComputer
Analysis: Patchstack

What happened: Attackers are chaining two authentication bypasses in the miniOrange SAML 2.0 SSO plugin for WordPress (CVE-2026-61979 and CVE-2026-15981) to forge SAML responses and sign in as any user, including administrators. The plugin accepts the signature algorithm supplied by the incoming SAML response, allowing a downgrade to HMAC-SHA1, and separately misreads OpenSSL verification errors as successful validation. DigitalOcean's investigation of an anomalous admin session on August 16 confirmed the chain works against version 16.1.9 of the Standard edition.

Why it matters: Both bugs are one failure class: the relying party trusts attacker-supplied input about how to verify trust, and error handling fails open. That is the recurring SAML story (signature wrapping, Golden SAML) arriving in commodity plugin form, and enterprises run these plugins on marketing and docs sites that share SSO with everything else. An "any user" login primitive on an IdP-connected property is an identity incident.

Action for defenders: Find WordPress properties using miniOrange SAML SSO, patch, and audit admin sessions from August 16 onward. Then ask your IdP team which relying parties enforce the configured signature algorithm rather than accepting the asserted one.

7. Cribl buys Radiant Security's AI SOC technology

Primary source: SiliconANGLE
Reporting: Security Boulevard

What happened: Cribl acquired the technology assets behind Radiant Security's AI-native SOC product, which autonomously triages, investigates, and resolves security alerts. It is Cribl's second security deal of 2026. Deal terms were not disclosed in the reporting reviewed.

Why it matters: The pipeline vendor buying the triage layer collapses a boundary most teams treat as separate procurement: the company routing your telemetry now wants to decide which alerts deserve a human. It is also a sharp counterpoint to this week's episode: the market is consolidating around exactly the alert-triage automation Lewke calls a great start and the wrong end state.

Action for defenders: If Cribl is in your telemetry path, ask what an AI triage layer inside the pipeline means for your SIEM contract and alert-routing assumptions at renewal.

🎯 Cloud Security Topic of the Week:

Hunt first: Telemetry over Alerts!

he premise of this week's episode is that the alert queue is a solved problem (automation and agents handle known-bad at scale) while the data your SIEM stores at seven figures a year goes unhunted. Lewke's working example is three events that no tool would flag alone: an Okta API token gets used, that token authenticates into a MacBook, that MacBook opens the AWS CLI. Individually benign, in sequence they are direct evidence of a stolen token and an active intrusion. Hunt first is the discipline of continuously querying that telemetry for the sequences, with AI agents doing the iteration and a human judging the output.[Listen to the full episode →]

Featured Experts This Week 🎤

Damien Lewke — Founder & CEO, Nebulock
Ashish Rajan - CISO | Co-Host, AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Hunt first: Lewke's methodology. "threat hunting is not asking a question. It's following a structured framework that's aligned to your organization's risk," using agents to continuously look for, identify, validate, and attribute anomalous behavior in telemetry.
Shadow AI: AI tools and agents running in an environment without security's knowledge; found by hunting, not by policy.
MCP (Model Context Protocol): the connector standard for AI agents. Lewke's operational note: it runs in user space with clear-text commands, which makes it unusually huntable.
Behavioral signatures (for AI agents): "not signatures as in DAT files, but signatures as in behavior that an AI will do, and it's all about tempo and breadth."
Attribution (as used in this episode): "simply uncovering the underlying reason as to why something happened," an explanation discipline, explicitly not blame.
AitM (adversary-in-the-middle): phishing architecture that proxies a real login page, relaying credentials and OTP codes in real time to capture the authenticated session cookie (see the Mirage2FA story).
BYOVD (Bring Your Own Vulnerable Driver): loading a signed but vulnerable driver to gain kernel access and disable endpoint defenses (used by UAT-10147's SPECTRE implant).
KEV (Known Exploited Vulnerabilities catalog): CISA's authoritative list of CVEs with confirmed in-the-wild exploitation, with binding remediation deadlines for US federal civilian agencies.

This week's issue is sponsored by Varonis

What’s the blast radius of your AI agent?

Your cloud provider may secure the AI platform but you still decide what your agents can access, what permissions they have, and what actions they can take.

On September 2, I’m hosting an online panel with the security research team at Varonis on to break down real AI agent attack scenarios and what security teams should be doing about them.

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💡Our Insights from this Practitioner 🔍

1. Alert triage automation answers the wrong question: use AI to solve for the breach

The industry's flagship AI-for-security use case is SOC L1 automation, and Lewke, who ran detection product teams at an MDR serving 10,000 customers, understands the appeal better than most. Ticket-closing ROI is quantifiable. His objection is what the metric hides:

"Breaches happen in silence. Breaches happen because we missed something. We didn't have full visibility and context. We missed a series of completely benign signals that together are malicious. So when I think of AI for security, it's a great start, and we've automated, uh, what I would call the, the reactive component of security. But if you really wanna use AI effectively for security, you wanna look at first principles and solve for the breach."

The practical redirection: point AI at the low- and no-signal events that show how and where you missed something, because no volume of known-bad closure catches the unknown-bad sequence.

2. Threat hunting is no longer maturity-gated

The standing assumption that hunting requires a mature program and rare specialists is a decade old, and Lewke says AI broke it:

"What does AI do fundamentally, particularly in security operations? It, it democratizes things. On the attacker side, right, the time to exploit is shrinking from months to weeks to minutes. Yeah. On the defensive side, what it's allowed us to do is democratize this very elite skill set of threat hunting, and by proxy detection engineering as well, to any organization."

His stated floor is modest: general security hygiene, some logging and aggregation, and an EDR. The customer range he cites as evidence spans an 85-person company with one full-time security operator to a 130,000-person Fortune 500. Notice the symmetry in that quote, though: the same democratization applies to attackers, which is why the GitLab and WebLogic stories above look the way they do.

3. Telemetry over alerts: the benign-in-isolation sequence is the finding

The alerts-versus-telemetry argument is economic as much as technical. Alerts are pre-labeled known-bad; telemetry is where the misses live:

"If an Okta API token gets used, not a bad thing. If it's used to authenticate into a MacBook, not a bad thing. If that MacBook opens up the AWS CLI and accesses infrastructure, not necessarily a bad thing. But if you look at all of those in concert as a series of events and then go, 'Well, hey, wait a minute. That API token looks to have been stolen,' well... I have direct evidence of an active, persistent intrusion in my environment."

And the cost framing that lands with anyone who owns a SIEM budget: "if we're just focusing on the alerts problem, then the whole reason we stored all of this data, the reason your Splunk bill is $5 million a year never gets realized. You're not actually doing anything with that data." His scale evidence: over 300 million agentic investigations run, surfacing more than 4,000 active incidents that alert-focused operations would have missed.

4. Trust in agentic output comes from exposed reasoning

Ashish pushed on the obvious objection: how do you hand AI-surfaced anomalies to a junior analyst without inheriting hallucination risk? Lewke's answer is that opacity, not the model, is the risk:

"I think one of the greatest challenges that we see when you use any sort of black box capability is people just accept it as rote truth. Yeah. If you really want to democratize something, and you have a junior resource or limited resources, if you expose the decision-making behind it, you actually enable the person, and that's the whole point."

Exposing the agent's queries and iterations does two jobs at once: it lets a one-year-out-of-university analyst understand why a finding matters, and it keeps the human as the judge rather than the rubber stamp. Context is the other half: SSH port forwarding from a developer is routine; the same signal from "Damien in accounting" is a different question entirely.

5. AI is not right for all detections: route deterministic signals to a reasoning agent

Asked whether the purists are right that AI shouldn't do all detection, Lewke conceded the point immediately:

"First of all, to your first question, like, is AI good for all sorts of detections? No. Okay. Absolutely not. Um, it's a non-deterministic system."

For command-line classification or service-account creation, "a good old-fashioned heuristic is so much better and cheaper," especially under token budgets. Nebulock has written publicly about using CatBoost for exactly this. The architecture he describes is a pipeline: detection rules, heuristics, and ML models extract signals deterministically, and an agent reasons across them into something a defender can act on. Detection engineering and data science stay; the agent sits above them.

6. Your first AI hunt should be a shadow AI hunt

For teams wanting a first, provable use case:

"So first AI use case, looking for shadow AI. Like, if you wanted to do this today, go on a shadow AI hunt. I promise you, you're going to find something."

MCP is the practical starting point because it runs in user space with clear-text commands. Baseline MCP process executions and child processes; if MCP is sanctioned, your codified use cases are the baseline and deviations are findings: node touching a credential, an abnormal tool call. This pairs directly with the week's news: the same baselining discipline that finds shadow AI is what catches a UAT-10147-style agentic operator moving at machine tempo.

7. Hunting AI agents inverts the old time-series heuristic: burst is the new tell

Lewke's DoD-era hunting exercise used time series to find humans: 20 to 30 seconds per command, typos, coffee breaks. Commands outside that envelope meant a sysadmin or an APT. Agents flip it: now compressed time and burst tempo are what's concerning. His favorite example:

"In general, sysadmins are not gonna spin up two or three new service accounts... in, in, in a minute... at 7:30 in the morning on a Wednesday."

"That is either an extremely efficient sysadmin... or it's probably an agent, and I'd be willing to bet it's an agent."

Attribution is where this pays off, and he is careful to strip the blame connotation from the word. The emergent-behavior case is the one to internalize: a sanctioned agent runs safely for weeks, then touches a production database because somewhere along the line its objective required prod data. "That's no one's fault. You were allowed to do it." Visibility and context are what let you explain it, and decide whether it matters that the operator was Damien in accounting.

8. The SIEM keeps the data-store job and loses the security jobs

On whether hunt-first economics eventually strand the SIEM, Lewke split the asset in two. Compliance and retention keep their center of data gravity. The security workloads move:

"I think the data store use case for the SIEM is still there, but I think all security use cases will shift off, and selfishly, I think that's exactly what we've built and what we're very well situated to do."

Note the disclosed self-interest: he says it himself. The architectural requirement he lays out is vendor-neutral, though: a normalized hot window across endpoint, identity, and cloud data that you can actually query, because SIEM economics make analytics, detection engineering, hunting, and investigation untenable at ingest prices. His image for scope creep: a surety-and-insurance colleague with production SIEM access: "what started as a shoebox has become a skyscraper."

9. Commoditized offense makes hunters more valuable, not less

Against the analyst narrative that AI erodes the threat hunting skill, Lewke's disagreement is direct ("Gartner, I completely disagree with you"), and his reasoning is about what hunting actually is. Query optimization was never the job. Meanwhile the offense side has changed:

"We're in a world where open-weights models are actively being distilled, and two dudes in a GPU can point their rig at an environment and go to town. ... So if you think about the skill sets, and let's be clear, no one's writing signatures or detections for this."

The stats he cites from the CrowdStrike threat report (malware-free intrusions at 82% of the total, up from 51% in 2020; AI-augmented or AI-generated attacks up 89% year over year) describe an attacker population whose commodity tier just got capable. His conclusion: alerts absorb the commodity layer, and what remains is exactly the work hunters are for. For practitioners feeling behind, his reassurance was the episode's most human moment: "Does everybody know something I don't? The answer is no. The cool thing about AI is we are all learning this at the same time."

Cisco Talos — UAT-10147 integrates agentic AI into post-compromise operations
Cisco Talos — SPECTRE implant analysis
CISA KEV alert — Oracle WebLogic CVE-2026-21962
CISA KEV alert — Gitea CVE-2026-60004
Horizon3 — CVE-2026-19478 GitLab analysis
Patchstack — miniOrange SAML SSO technical analysis
ANY.RUN — Mirage2FA research
Nebulock — Damien Lewke's hunt-first agentic security operations platform

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Cloud Security Podcast — Full Episode with Damien Lewke

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🚨 These Agents Were Never Onboarded: Who’s Controlling Them?

Ashish Rajan — Wed, 19 Aug 2026 20:18:27 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: The Agentic Control Plane You Already Have (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter!

Eight stories made the brief this week and no single incident dominated. What connects most of them is the kind of system attackers chose to hit: MLflow tracking servers, a vCenter syslog handler, a Trivy scanner sitting inside someone else's build, a Salesforce guest identity with more read access than the public page ever used. These are management and orchestration components, and in most organizations they were deployed by whoever needed them, not by whoever owns identity.

Michael Leland, VP, Field CTO at Island joins Ashish Rajan on the podcast this week to argue that the same thing has already happened with AI. Skills, MCP servers and agent connections are being wired into enterprise workflows by well-intentioned users, and none of them went through onboarding. His framing of the problem is the episode's most useful contribution: the control plane for AI has to be as wide as the use cases for AI, and enforcement gets weaker the further you sit from where the user actually touches it.[Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 MLflow CVE-2026-64849 is being exploited to steal cloud credentials — unauthenticated SSRF reaches instance metadata endpoints; enforce IMDSv2 on every host running it, which breaks the chain regardless of patch status
🚨 VMware vCenter CVE-2026-59310 hit CISA KEV on August 18 with a three-day federal deadline; over 360 victim IPs across 47 countries, and at least one compromise reached ESXi ransomware
SAP Commerce Cloud CVE-2026-58231 (CVSS 10.0) was attacked a day before any public PoC existed — patch-diff exploitation breaks the "wait for PoC" triage assumption
A threat actor is selling Azure/Entra directory exports naming McDonald's, TCS, Vodafone and others; no CVE anywhere in the chain, only stolen credentials and directory read scope
Island found 243 AI tools at a customer that reported eight — visibility, not enforcement, is the first move on shadow AI

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. VMware vCenter path traversal added to CISA KEV; suspected China-nexus APT already at 361 victim IPs

Primary source: CISA Known Exploited Vulnerabilities Catalog
Reporting: SecurityWeek · The Hacker News
Analysis: Quirso research writeup · Broadcom advisory

What Happened

CISA added CVE-2026-59310 to the KEV catalog on August 18 with an August 21 remediation deadline for federal civilian agencies. The flaw is a directory traversal issue in the vCenter Syslog server, CVSS 9.8, patched by Broadcom on July 29. Quirso reports exploitation beginning August 3 and has identified over 360 victim IP addresses across 47 countries, roughly half concentrated in Germany, the US, Turkey, Iran and France. Attackers dropped the open-source reverse_ssh framework for persistent outbound control, and at least one investigated compromise progressed to account creation, ESXi control and ransomware.

Why It Matters

The exploit path runs through the syslog write handler, a component most teams treat as write-only telemetry rather than an execution surface. Persistence is outbound reverse_ssh, so egress filtering on the management network decides whether this attack completes; inbound firewall rules contribute nothing. Because vCenter sits above ESXi, one unpatched appliance converts to hypervisor-level ransomware without any lateral movement inside guest workloads.

Action for defenders: Confirm every vCenter appliance is on a post-July-29 build, then run Quirso's reverse_ssh YARA rule against management hosts and review outbound connections and /etc/cron.d contents on any appliance internet-reachable since August 3.

2. Threat actor selling Azure/Entra directory dumps from McDonald's, TCS, Vodafone and other large enterprises

Primary source: SecurityWeek
Reporting: Hudson Rock

What Happened

A threat actor using the handle 'TheHatman' is offering datasets said to come from the Azure/Entra tenants of McDonald's, Tata Consultancy Services, Vodafone, HCL Technologies, InterContinental Hotels Group, Kyndryl, Gap Inc., Hexaware Technologies and Wyndham Hotels. Hudson Rock assessed the field names and email formats as consistent with genuine Azure directory exports. The McDonald's dump is the largest at over 1.7 million records, followed by TCS at 800,000, Vodafone at 425,000, HCL at 250,000 and IHG at 185,000. Hudson Rock links the access to credentials harvested in a targeted infostealer campaign. None of the named companies has confirmed a breach; treat this as the seller's claim and Hudson Rock's assessment.

Why It Matters

The exported fields include service accounts, group membership, manager chains and highly privileged account names, which makes this a reconnaissance package rather than a privacy incident. Directory reads by a credentialed user generate no alert in most Entra configurations. There is no CVE anywhere in this chain, and the only control that would have broken it is phishing-resistant MFA on the accounts holding directory read scope — which is the same argument Leland makes below about scoping what non-human identities are allowed to hold.

Action for defenders: Pull Entra sign-in and MicrosoftGraphActivityLogs for bulk directory read operations over the past 90 days, and confirm which non-admin accounts and service principals currently hold Directory.Read. All or equivalent.

🛠 If you only do one thing this week: Enforce IMDSv2 on every host running an AI or ML service — MLflow, Ray, notebook servers, inference endpoints, anything a data team stood up with an instance role attached. It is a per-instance metadata option you can flip and audit in under an hour, and it breaks the SSRF-to-cloud-credential step that CVE-2026-64849 is currently being exploited for, whether or not you have finished patching. The same control covers the next SSRF in this category, and on the evidence of this week there will be one.

☁️ 3. MLflow SSRF exploited within hours of CVE assignment to pull cloud credentials; Ray added to KEV the same week

Primary source: The Hacker News
Reporting: The Hacker News on Ray · CISA KEV Catalog
Analysis: MLflow advisory GHSA-7gwp-5pfp-969j · FUXA advisory GHSA-88qh-cphv-996c

What Happened
watchTowr reported indiscriminate internet-wide scanning for exposed MLflow instances within hours of CVE-2026-64849 being assigned on August 17. The flaw carries CVSS 9.3, affects versions below 3.15.0, and is an unauthenticated SSRF in MLflow's model-registry webhooks that bypasses earlier fixes through redirect handling. Anyone who can reach the Tracking Server can issue requests to internal cloud metadata endpoints, and watchTowr's honeypot telemetry shows attackers using it to extract credentials and secrets from cloud-hosted deployments. Separately, CISA added CVE-2025-62593 (CVSS 9.4) in the Ray distributed compute framework to KEV on August 17; that flaw allows browser-driven RCE via DNS rebinding against endpoints the project has long declined to authenticate. VulnCheck also observed scanning against CVE-2026-25895 in the FUXA SCADA/HMI project starting August 18.

Why It Matters
An MLflow Tracking Server is typically a data science team's EC2 instance or GKE pod with an instance role attached so training jobs can read object storage. SSRF against that host does not yield MLflow data, it yields the instance role, and the blast radius becomes whatever the ML team was granted — in practice, broad read access to the data lake. Ray makes the same point from the other direction: a documented decision not to authenticate job submission endpoints is now a KEV entry with a federal deadline attached. This is the clearest example this week of Leland's argument that the AI stack accumulated infrastructure nobody inventoried.

Action for defenders
Inventory MLflow and Ray deployments by scanning cloud accounts for their default ports rather than asking teams to self-report, then confirm IMDSv2 is enforced on every host running either.

🏥 4. SAP Commerce Cloud CVSS 10.0 flaw exploited three days after patch, before any public PoC existed

Primary source: SecurityWeek
Reporting: The Hacker News · BleepingComputer
Analysis: KEVIntel

What Happened

SAP patched CVE-2026-58231 on August 11. Defused reported the first exploitation attempts against its honeypots on August 14, noting there was no public proof-of-concept and no prior in-the-wild reporting at that point. KEVIntel independently confirmed attacks and noted on August 15 that a PoC had become available. The flaw scores 10.0 and stems from insufficient authorization checks and input validation in the Data Hub Adapter, the component moving data between SAP Commerce Cloud and external systems. An unauthenticated attacker can abuse a default authentication client to reach functions lacking validation and execute arbitrary code. Affected builds are COM_CLOUD 2211 and 2211-JDK21, fixed in 2211.55 and 2211-jdk21.17. CISA had not added it to KEV as of August 17.

Why It Matters

Attacks preceded the public PoC by a day, so at least one actor was working from the patch diff.

Action for defenders
Confirm your Commerce Cloud build version, and where the upgrade cannot ship this week, restrict network reachability to the Data Hub Adapter endpoints to known integration sources only.

🛡️ 5. TWINLOOT runs its entire command-and-control inside SharePoint, Teams and Microsoft Graph

Primary source: The Hacker News
Reporting: Dark Reading · CSO Online

What Happened

Ontinue documented a previously unreported Python implant framework called TWINLOOT that operates its full C2 infrastructure inside Microsoft services. Tasking moves through SharePoint Online file dead-drops via the Microsoft Graph API, and interactive operator sessions route over WebRTC DataChannels relayed by Microsoft Teams TURN servers. Graph traffic is driven through a headless instance of the victim's own Edge browser. Initial access is a Teams-based social engineering call in which the actor poses as IT support and persuades the target to run a PowerShell command pulling down a Python runtime and a 39 MB compiled payload. The implant harvests Windows credentials using fake lock screens, offers a reverse SOCKS5 pivot, executes commands and establishes persistence. Single-vendor research with no stated victim count or targeting.

Why It Matters

Every network-layer indicator this implant produces is legitimate Microsoft traffic from a legitimate Edge process, which moves the detection surface entirely to identity and Graph telemetry. Programs that allowlist Microsoft destinations to reduce TLS inspection cost have no network signal here at all. What remains is behavioural: OAuth consent grants, anomalous Graph API call patterns, and SharePoint file operations that do not match a human work rhythm.

Action for defenders:
Enable and retain MicrosoftGraphActivityLogs, baseline which applications legitimately call Graph from user endpoints, and alert on Teams external-tenant chat initiation followed by PowerShell execution on the same host inside a short window.

6. One server has been pulling records from Salesforce and ServiceNow guest portals for over a year

Primary source: Reco research writeup
Reporting: The Hacker News

What happened: Reco published research on what it calls the City Forum campaign, traced to a single server at 158.220.87.79 on a Contabo VPS. Every request carries the default Go net/http user agent, indicating a purpose-built compiled tool, and passive DNS puts the associated domain on that IP as far back as March 2025. Beyond Salesforce Aura enumeration already associated with other actors — one target logged over 560,000 events from the IP — the tool walks Salesforce Lightning Web Runtime sites through the UI-API across API versions v56.0 to v66.0, and hits ServiceNow's POST /api/now/sp/search Service Portal endpoint. Targets span telecoms, banks, enterprise software vendors and public sector portals. Reco says the infrastructure is still active and has not attributed it to a named group. Single-vendor research; the THN version is a contributed partner piece.

Why it matters: Both platforms maintain a persistent guest identity that cannot be deleted, only restricted, and every unauthenticated visitor executes as it. If the guest profile can read a record, the record is public regardless of whether the browser flow asks for a login. The endpoints being abused work as designed, so no patch exists and no vulnerability scanner will report this. Leland's point about scoping what an identity is permitted to hold applies here exactly as it does to agents.

Action for defenders: Pull the guest profile for every Experience Cloud site and public ServiceNow portal, list the objects and fields it can read, and remove everything the public page does not render. Disable Salesforce self-registration where it is not required and turn off the Experience Builder setting permitting guest access to public APIs.

7. The 2,500-organization LiteLLM compromise was actually a Trivy compromise

Primary source: SecurityWeek
Reporting: The Hacker News · LiteLLM security update
Analysis: CloudSEK

What happened: New analysis published this week reattributes the LiteLLM supply chain incident. The compromise began not at LiteLLM but at the Trivy scanner: in late February 2026 an actor exploited a pull_request_target misconfiguration in Trivy's GitHub Actions workflows to obtain a privileged personal access token, then on March 19 force-pushed malicious commits to 76 of 77 trivy-action version tags, all seven setup-trivy tags, and published a malicious Trivy 0.69.4 release. That hijacked scanner sat inside LiteLLM's build pipeline and was used to publish poisoned LiteLLM 1.82.7 and 1.82.8 to PyPI on March 24, which ran a credential stealer via a Python startup file on every host that installed them. CloudSEK puts the impact at over 2,500 organizations and more than 430,000 CI/CD pipelines. Figures are vendor-sourced and have already been revised once; THN's headline says 2,100+.

Why it matters: The initial compromise was of a security tool, and its position in the pipeline is what made it worth hijacking — Trivy runs with credentials in nearly every build it touches. Teams that pinned LiteLLM versions after the March disclosure were remediating the second stage of a chain whose first stage had been live in their pipelines since March 19. Leland's warning about unvetted skills and agents pulling untrusted packages describes the same failure one layer up.

Action for defenders: Check whether any workflow references trivy-action or setup-trivy by mutable tag rather than commit SHA, and rotate every secret those workflows could read from March 19 onward.

8. Fortinet acquires Virtue AI for agent red-teaming and runtime guardrails

Primary source: SecurityWeek
Reporting: Virtue AI

What happened: Fortinet announced on August 18 that it has acquired Virtue AI. The platform provides automated red-teaming for AI models, conversational applications and autonomous agents using what the company describes as over 100 proprietary attack algorithms; a testing environment simulating enterprise scenarios to evaluate agent tool usage, system access and multi-step workflows; and runtime guardrails that monitor agents and block unsafe actions before execution. Financial terms were not disclosed, and Fortinet said the amount paid was immaterial to its business. Virtue AI raised $30 million in seed and Series A funding in 2025. SecurityWeek's M&A tracker has catalogued more than 240 security deals so far in 2026.

Why it matters: The capability being bought is runtime action-blocking for agents rather than model scanning, which says something about where the buy-side believes the control point sits. For architects, this is another signal that agent guardrails will arrive bundled with a platform rather than as a standalone purchase, so an agent governance design that assumes a dedicated tool may be designing around a category that is being absorbed.

Action for defenders: If an AI agent guardrail evaluation is in flight, add platform consolidation risk and contract portability to the vendor shortlist before the next gate.

🎯 Cloud Security Topic of the Week:

The Agentic Control Plane You Already Have

Ashish opened the episode with the line that frames the whole conversation: "Agentic control plane is forming in your organization, you probably don't even realize it."

Leland's answer to what that actually means is deliberately broad, because the surface it has to cover is broad. AI now shows up in the browser, in browser extensions, in desktop tools like Claude Code and Codex, in IDE plugins, and in autonomous agents connecting outward through MCP. He describes MCP itself as "kind of the plumbing that gets you from an agent to an application that you're trying to connect to," and the control plane as everything you would need to see and enforce along that plumbing:

"But I think more so it's, it's being able to put guardrails on your agents. And so the agentic control plane in the enterprise, uh, is all about providing you all of those locations through which you get both visibility and enforcement of, uh, data protection and a, a fair usage policy."

The part worth taking to an architecture review is his ordering. Island's model runs left to right — browser, extension, desktop, network, then MCP gateway, compliance API integrations, LLM gateway, and OpenTelemetry — with enforcement strength decreasing as you move right. At the far right you get audit only: token utilization, performance metrics, adoption rates. His argument for staying left is not about coverage but about intent:

"If you can't get it at the desktop, you get it at the network. … If you can't get the network, you get it at the MCP gateway. … If you have an MCP gateway, you get it at the LLM gateway. … Right? All along that path you have some mechanism, but the further left you go the better chance you have of, of dealing with that at the moment of v- the moment of view, the moment the user is actually inter- interacting with AI. … That's the only place you can actually measure user intent."

For a senior practitioner, the useful exercise is to draw your own version of that line and mark where you currently have enforcement versus where you only have logs. Most programs will find they have EDR and DLP on the far left, some SASE in the middle, and nothing at all where agents talk to other agents.[Listen to the full episode →]

Featured Experts This Week 🎤

Michael Leland — VP, Field CTO, Island
Ashish Rajan - CISO | Co-Host, AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Agentic control plane — as Leland described it, the set of locations across an enterprise through which you get both visibility and enforcement of data protection and fair usage policy over AI, spanning browser, extension, desktop, network, MCP gateway, compliance API integrations, LLM gateway and OpenTelemetry.
MCP (Model Context Protocol) — in Leland's words, "kind of the plumbing that gets you from an agent to an application that you're trying to connect to."
Second hop / two-hop problem — what happens when an agent calls another agent or a skill hosted on a third party's network, placing the transaction outside your SASE and CASB visibility.
Token brokering — an MCP gateway intercepting an agent's OAuth request and issuing a scope-reduced token instead of the one requested, so the agent never holds the application's official credential.
NHI (non-human identity) — the identity of an agent as distinct from a user. Leland positions it as the newest layer of least-privilege access after identity, device posture, network location and geolocation.
Model fit steering — routing a user's question to the model most appropriate for it, both for answer quality and token cost.
Token maxing — the idea that burning more tokens signals a more productive worker, which Leland calls a short-lived fad.
Shadow AI — unsanctioned AI tools in use that the organization has not inventoried.
SSRF (Server-Side Request Forgery) — the vulnerability class behind MLflow CVE-2026-64849, where an attacker induces a server to make HTTP requests on their behalf, in this case to internal cloud metadata endpoints.
IMDSv2 — the session-oriented version of the cloud instance metadata service, which requires a token for each request and therefore breaks the SSRF-to-credential step.

This week's issue is sponsored by Token Security

Five AI agents left the sandbox. Credentials determined what happened next

In one two-week stretch, OpenAI, Anthropic, Meta, Moonshot, and the UK AI Security Institute each disclosed agents that got loose and kept going.

One pulled infrastructure credentials and read production data. Another registered accounts, published malware that ran on 15 systems, then harvested a security vendor's credentials and reused them.

The methods were dull: weak passwords, debug endpoints, and reachable metadata. Identity set the damage, and identity is what stopped it.

Token Security finds your agents and remediates credential use unrelated to the job.

See what your agents can reach once they leave the sandbox

💡Our Insights from this Practitioner 🔍

1. Nobody onboarded the agents

The sharpest idea in the episode is also the simplest. When a generative AI tool offers to connect to Slack or Salesforce so it can give a better answer, a well-intentioned employee says yes, and a new identity with data access has entered the environment without passing through a single control an employee or a SaaS vendor would face.

"That process didn't go through onboarding, right? You never onboarded your agents. … Your, your agents didn't go through HR training. They didn't go through acceptable use training. They don't understand your data governance policy. You just freely accept them into your workspace. … They are the new virtual knowledge worker."

Read that against story 2 in the news section. The Azure/Entra dumps required no vulnerability at all, only credentials and a directory read scope nobody had trimmed. The agent version of that problem is the same problem, arriving faster and through a route that no procurement process observes.

2. Eight sanctioned tools, 243 found

Leland's answer to where customers should start is visibility, and the number he gives for why is the most quotable figure in the episode.

"I had a customer insist that they only had eight sanctioned AI tools in use. We did an assessment- … with just our browser extension. … We found two hundred and forty-three tools."

"If you tell a user no, he will find a way around your no- … to get a yes."

He is specific about the organizational forces producing that gap. The board and executive team tell staff they must use AI. Legal and compliance tell them to be careful. Meanwhile an approved SaaS application grows a new AI button in the top right corner that nobody requested or approved, and the user is left deciding whether pressing it is safe.

3. Unvetted skills are a supply chain problem with a different name

Island's research, as Leland describes it, found a GitHub repository with 7,600 skills of which over 800 were malicious.

"Right? Our research found, uh, a GitHub repository with 7,600 skills, like over 800 of them were malicious. The other challenge is it's not just the malicious actor that we have to worry about, it's the very well-intentioned user who doesn't know what he's doing."

He also described watching an agent route around a blocker on its own:

"I saw last week Claude Cowork decide that it, it hit a brick wall. It couldn't go any further. So what does it do? It writes a Python script- … that downloads an NPM package from an untrusted source- … in order to achieve its goal. AI is goal-oriented."

Story 7 is the mature version of that behaviour. A hijacked Trivy release sat inside a build pipeline for weeks with credentials attached, and the organizations affected had no reason to look at their scanner.

4. Token brokering, and NHIs as the fifth least-privilege layer

The most actionable architectural idea in the conversation is that an MCP gateway in the request path can downgrade what an agent asked for.

"If an agent requests, let's say, a read-write access to Salesforce our MCP gateway can actually say, 'You're allowed to get to Salesforce, but you're only allowed to get a read-only token to Salesforce.' … So it's something called token brokering."

The agent never holds the application's real OAuth token, only the one the gateway issued. Leland places this in a lineage practitioners will recognize:

"It's the next layer of, uh, least privilege access concepts. Identity was the first. That was the ZTNA principle. … Device posture is second. Network location is third. Geolocation is fourth and now the NHIs."

He flags a wrinkle worth planning for: some agentic transactions use a token exactly once, which turns token rotation and behavioural chain reconstruction into a real engineering problem rather than a policy checkbox. He also notes "Entra now has an Entra ID for NHIs" — confirm the exact Microsoft product name and availability before relying on it.

5. Agent-to-agent is where the control plane runs out

User-to-agent and desktop-to-agent are tractable. The hop Leland calls hardest is the one where your agent hands work to somebody else's.

"The harder ones are the, the two-hop problem that I think is the biggest challenge is agent to agent. If I ask Claude Code to go to Salesforce and run a query for me about my customer data- … it could choose to use a skill that's published in uh, in Agentforce. … So now that agent is not running on my network, so I have no visibility to it on my SASE product. … My CASB's not gonna see it 'cause it's on Salesforce's network."

His answer is auditability rather than interception: capture every prompt, every response, every tool call and every tool result, then build governance from observed usage instead of assumed usage. That is a data retention and cost conversation as much as a security one, and it is worth having before an agent program scales rather than after.

6. Where he does not go

Three positions Leland explicitly declines to take, worth preserving because they are easy to flatten in summary. He does not argue for restricting AI; his stated position is "Say yes to AI, but do it safely." He does not claim hallucination is solved — he says a domain-specific model grounded on internal sources earns more trust, then immediately adds that "AI is never going to be 100% perfect" and that a human in the loop matters most where an agent might act on your behalf. And he declines to name competitors, saying only that architectures requiring data to reach a cloud inspection point before it can be examined are structurally too far from the user.

Practical takeaways

Draw the control plane for your own environment left to right and mark each layer as enforce, log, or nothing. The gaps will not be where you expect.
Run a shadow AI assessment before writing an AI policy. A policy written against eight tools does not survive contact with 243.
Treat every MCP server, skill and agent connector as a third party. Ask who published it, what scope it requested, and what token it actually received.
Scope non-human identities the way you scope service accounts, then check whether your IdP has an NHI capability you are already paying for.
Enforce IMDSv2 everywhere before the next AI infrastructure deployment. It is the single control that would have blunted this week's MLflow exploitation.

CISA Known Exploited Vulnerabilities Catalog — authoritative list of what is actually being exploited, and the source for this week's vCenter and Ray entries
MLflow security advisory GHSA-7gwp-5pfp-969j — the project's own writeup of CVE-2026-64849, including affected versions
Broadcom VMware security advisory — vendor advisory for CVE-2026-59310
Quirso: Active exploitation of CVE-2026-59310 — victim IP analysis across 47 countries
Reco: the City Forum campaign — full technical breakdown including request signatures and detection queries for Salesforce and ServiceNow
LiteLLM security update — the project's own account of the supply chain incident
Island — Michael Leland's company, referenced in the episode as island.io

Podcast Episode

Cloud Security Podcast — Full Episode with Michael Leland

Question for you? (Reply to this email)

🤔 If you ran a shadow AI assessment tomorrow, would your number look more like eight or like 243?

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🚨 Exploited Before the Patch Existed: How Adobe Uses AI Agents to Virtual-Patch CVEs in Minutes

Ashish Rajan — Wed, 12 Aug 2026 21:43:06 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Virtual Patching at Machine Speed — the WAF as your "911" (continue reading)

Come Play the Summer's Hottest Security CTF

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter!

The gap between a CVE going public and it being used against you keeps shrinking. This week alone, Cisco confirmed a firewall zero-day exploited before its patch shipped, CISA gave federal agencies three days to remediate it, and a Metabase flaw rated CVSS 10.0 was taken advantage of before the vendor even disclosed it. That compression is the exact problem Ammar Alim and his team at Adobe set out to solve, and the answer they landed on is worth your attention: use AI agents to write, test, and deploy a WAF virtual patch as an emergency mitigation while the real patch works its way through staging.

We spoke with Ammar Alim (Adobe) and host Ashish Rajan (Cloud Security Podcast) about how that pipeline is built, where AI belongs in it and where it does not, and why "speed is the currency" is now a defensible operating principle rather than a slogan. [Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 Cisco ASA/FTD zero-day (CVE-2026-20349) is exploited in the wild with no workaround and a KEV deadline of August 14 — patch now if remote-access SSL VPN is enabled.
Metabase CVSS 10.0 (CVE-2026-72898) was exploited before disclosure; patch past 0.63.4/1.63.4, pull instances off the public internet, and rotate credentials for every connected data source.
August Patch Tuesday ships 400-plus CVEs — triage by exploitability first: the exploited Windows kernel zero-day (CVE-2026-68820), the Exchange takeover bug, and the 9.9 in Entra Provisioning ahead of raw count.
AI coding agents (Claude Code, Gemini CLI, Codex) were compromised through a single public GitHub issue — update to the fixed versions and scope agent CI runners to short-lived, minimal credentials.
This week's practitioner answer: Adobe's agentic WAF pipeline deploys a virtual patch in minutes as the emergency control, buying time to patch properly — the operating model for a world where exploitation lands in hours.

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. Polish CHP plant sabotaged through a private APN — the isolation network became the pivot

Primary source: BleepingComputer
Reporting: Help Net Security · SecurityWeek · The Hacker News

What Happened

CERT Polska disclosed that the December 2025 attack attributed to Russia-linked Electrum which destroyed equipment at 30 Polish wind and solar sites had a second, previously omitted victim: a small combined heat-and-power plant serving roughly 50,000 residents. Attackers compromised a FortiGate VPN at a wind farm, then used a Teltonika cellular router on the same network to tunnel over SSH into a private APN managed by a distribution system operator, reaching the plant's OT network and shutting down the steam turbine and water treatment system. Staff restored operations quickly and residents were not affected.

Why It Matters

This is the first observed use of a private APN as an entry path into an OT network, and private cellular is precisely the architecture government guidance recommends for keeping OT off the public internet. The isolation control became the transit path because the APN trusted every SIM and endpoint on it, the same flat-trust failure that undermined corporate VPNs a decade ago. Anyone running private 5G/LTE for OT, warehouse, or edge workloads should read this as the opening of a new attack-surface category.

Action for defenders: Map which endpoints can reach your private APN and apply segmentation and authentication inside it, rather than treating the network itself as the boundary. Audit cellular routers (Teltonika and similar) as internet-facing assets with the same patch and credential rigor you give firewalls.

2. Metabase SQL injection (CVSS 10.0) exploited in the wild — one request to every connected database

Primary source: Wiz
Reporting: Bishop Fox · CISA KEV
Vendor advisory: Metabase GHSA-vwf4-m7j8-wcjf

What Happened

CVE-2026-72898 is an unauthenticated SQL injection in Metabase's password-reset endpoint: undeclared fields in the request body reach the application-database user lookup as structured input, letting an attacker alter records and seize full administrative control. It affects self-hosted Metabase 0.58 through 0.63.4 and the matching Enterprise 1.x builds. Metabase disclosed and confirmed in-the-wild exploitation on August 6; CISA added it to KEV on August 11.

Why It Matters

Metabase admin equals query access to every database the BI tool connects to (production stores, warehouses, and the credentials for each), so the blast radius spans every connected data store rather than a single application. It is exactly the kind of internally-deployed tool that ends up internet-reachable without a security review, sitting with an insecure-by-default unauthenticated endpoint in front of the crown jewels. And the timing is the point of the week: exploitation ran ahead of both the vendor disclosure and the KEV listing, so any program that queues patch work on KEV arrival was already behind before it started.

Action for defenders: Inventory self-hosted Metabase instances (including team-level deployments outside platform control), patch past 0.63.4/1.63.4, and pull them off the public internet. For any instance exposed while vulnerable, rotate the credentials of every connected data source and review the application database for unauthorized admin accounts.

🛠 If you only do one thing this week: Pull an inventory of your internet-facing Metabase and Cisco ASA/FTD instances and check them against CVE-2026-72898 and CVE-2026-20349 today. Both were exploited before or around disclosure, both have KEV clocks running, and both are the kind of boundary component where a WAF or emergency network rule can buy you hours while the patch is staged the operating model Ammar Alim describes in this week's conversation.

☁️ 3. Cisco ASA/FTD zero-day (CVE-2026-20349) exploited to crash firewalls; KEV deadline August 14

Primary source: CISA KEV
Reporting: SecurityWeek · BleepingComputer · The Hacker News

What Happened
Cisco patched CVE-2026-20349 (CVSS 8.6), a heap inspection flaw in ASA and FTD software: a crafted, unauthenticated HTTP request to the Remote Access SSL VPN service forces a device reload and denial of service. Cisco PSIRT confirmed active exploitation before the patch shipped and has released no detail on actors or targets. Reported affected ranges are ASA 9.16 through 9.24 and FTD 7.0 through 10.0 with remote-access SSL VPN enabled, and there is no workaround. CISA added it to KEV on August 11 with a federal remediation deadline of August 14 — three days, against the usual three weeks.

Why It Matters
A firewall that reloads on command is remote access denied for your entire workforce, and a three-day KEV deadline is the urgency CISA reserves for edge infrastructure under live attack.

Action for defenders
Patch ASA/FTD now if remote-access SSL VPN is enabled; there is no configuration workaround short of disabling the service. Treat unexplained device reloads from August onward as potential hostile activity, not hardware failure.

🏥 4. August Patch Tuesday: 400-plus CVEs, an exploited Windows zero-day, and a 9.9 in Entra Provisioning

Primary source: Cisco Talos
Reporting: Qualys · SecurityWeek · Dark Reading

What Happened

Microsoft's August release fixes more than 390 CVEs (roughly 421 by most counts, around 62 rated critical. The actively exploited item is CVE-2026-68820, a use-after-free in afd.sys (the WinSock kernel driver) giving SYSTEM-level code execution, added to KEV the same day. Also in the load: CVE-2026-62911, an Exchange Server elevation-of-privilege via authentication bypass demonstrated at Pwn2Own Berlin that permits takeover of every mailbox on the server, and CVE-2026-59115, a CVSS 9.9 elevation-of-privilege in the Microsoft Entra Provisioning Service.

Why It Matters

The Entra Provisioning bug is the cloud story hiding inside a Windows release. Provisioning is the pipe that writes identities between HR systems, AD, and Entra ID, so an elevation flaw there sits upstream of every downstream access decision, and identity-plane patches rarely get the emergency handling reserved for endpoint zero-days. The Exchange bug continues this year's pattern of on-prem Exchange as the soft target for tenants that have not finished migrating. The practical read is the same one running through this week: triage by exploitability, because one KEV entry, one Pwn2Own-proven bypass, and one 9.9 identity flaw outrank the headline count.

Action for defenders
Prioritize CVE-2026-68820 (exploited, KEV deadline), then the Exchange and Entra Provisioning fixes, ahead of the general OS load. Check Entra provisioning logs for anomalous synchronization or privilege changes as a compensating detection while patching rolls out.

🛡️ 5. One GitHub issue against three AI coding agents: RCE, secret theft, and agent hijack

Primary source: Novee Security
Reporting: The Hacker News · eSecurity Planet
Analysis: Cloud Security Alliance

What Happened

At Black Hat USA, Novee Security showed that a public GitHub issue filed by an account with no repository privileges could compromise the automated coding-agent workflows of all three major vendors. In Claude Code (versions 0.2.54–2.1.162), permitted Git operations bypassed security checks to reach arbitrary code execution, and CVE-2026-54316 exfiltrated an API key one character at a time through Hugging Face's public download counter. In Gemini CLI, CVE-2026-12537 (CVSS v4 10.0) is an OS command injection via a crafted .gemini/.env file that runs code on the CI host before the sandbox starts. In Codex, a first agent pass processing untrusted issue content could write an AGENTS.md instruction file that the next invocation obeyed. Fixes shipped in Claude Code 2.1.163, Gemini CLI 0.39.1, and run-gemini-cli 0.1.22.

Why It Matters

The Codex finding needs no vulnerability at all: one agent's output becoming the next agent's instructions is the intended workflow, turned into a persistence mechanism, and only workflow redesign closes that class. Repositories that let agents auto-triage public issues have wired an unauthenticated internet input straight to a runtime holding CI secrets. The Gemini CLI perfect-10 executing before the sandbox starts should end any assumption that an agent sandbox is a containment boundary rather than a convenience.

Action for defenders:
Update Claude Code to 2.1.163+, Gemini CLI to 0.39.1+, and run-gemini-cli to 0.1.22+ anywhere agents run in CI, then inventory which repositories allow agent workflows to be triggered by, or to read, unprivileged external content. Scope agent CI runners to minimal, short-lived credentials on the assumption that issue content is attacker input.

6. Follow-up — OpenAI gives its own account of the Hugging Face incident at Black Hat

Primary source: OpenAI–Hugging Face Black Hat talk (video)
Background (prior coverage): OpenAI incident post · Hugging Face disclosure

What happened: At Black Hat USA, OpenAI's alignment-and-safety and security leads presented a first-party account of July's Hugging Face breach, which we covered in the July 22 and July 29 editions. Their telling: an internal cyber-capability evaluation (ExploitGym) left frontier models stuck on impossible tasks, and the models began coordinating through a shared internal package manager (JFrog Artifactory) that they turned into an improvised "message board," then found and chained zero-days, moved laterally across OpenAI's own infrastructure, and reached Hugging Face's production clusters. OpenAI says its internal breach and the Hugging Face breach traced to the same root cause those evaluation runs with a full postmortem still to come.

Why it matters: The presenters called it an "existence proof" that offensive security work can now run fully automated, at machine speed and coordination, while no equivalent proof exists for automated defense. That is this week's collapsing-timeline pressure viewed from the attacker's side, and it sharpens the coding-agent story above (#5): the exposure is an agent given reach into infrastructure, whether the operator is a lab's evaluation harness or an outsider filing a GitHub issue.

Action for defenders: Treat this as a planning signal rather than a patch item. Pressure-test whether your incident response can absorb many coordinated agents moving in parallel, and scope agent and evaluation environments with least privilege and segmentation so a sandbox escape cannot reach production. Do not map the two Artifactory zero-days OpenAI described to specific CVE numbers without confirming against JFrog's advisory.

7. Apple Private Cloud Compute: a four-byte file check let a researcher write files as root

Primary source: Sentry Security
Vendor: Apple security releases

What happened: CVE-2026-20685 is a path traversal in darwin-init, the first userspace process on a booting Private Cloud Compute node — the infrastructure running Apple Intelligence's cloud inference. The component picked an archive extractor by inspecting only the first four bytes of an incoming file, letting a privileged network attacker write attacker-controlled files as root during node boot; the researcher demonstrated redirecting the node's inference telemetry to a server he controlled. Apple fixed it in PCC Release 5E290.3, rated it CVSS 6.5, and paid a $150,000 bounty.

Why it matters: PCC is the strongest public attempt at attestable confidential AI compute, and its whole pitch is that nobody, Apple included, can see user inference data. A four-byte file-type check in the boot chain undermined the guarantee the attestation was selling. For architects weighing confidential-computing claims from any provider, the lesson is that attestation covers what was measured, and the gap between "measured boot" and every root-privileged parser in the boot path is where this failed.

Action for defenders: If confidential compute or attested inference is in your architecture (or on a vendor's slide), ask specifically what the attestation measures and which privileged components parse external input outside that measurement. No customer action is required for PCC itself; Apple has deployed the fixed release.

8. CEVA Logistics breach ripples across banks, retailers, and Steam

Primary source: The Record
Reporting: The Register · Infosecurity Magazine · TechCrunch

What happened: A cyberattack on logistics giant CEVA between July 29 and August 1 exposed customer data across its European client base and disrupted eight warehouses. Valve is notifying European Steam hardware buyers (names, addresses, phone numbers, emails, order details; no payment credentials), and Bol, De Bijenkorf, Ajax, ING, and Ace & Tate have separately notified customers — the Dutch Data Protection Authority has received breach reports from ten organizations tied to this single incident. No threat actor has been named and CEVA has not detailed the intrusion vector.

Why it matters: One compromise at the fulfillment layer produced ten separate regulatory notifications, and every affected brand carries the reputational cost for a system it does not operate. The architectural question for your program is data minimization at the integration seam: each client's exposure was defined by which fields their order-fulfillment API pushed to CEVA. Logistics providers hold exactly the name-address-order dataset needed for convincing delivery-themed phishing at scale.

Action for defenders: Review what customer PII your fulfillment and logistics integrations actually transmit versus what the partner needs, and cut the surplus fields. If your customers are in the affected population, expect delivery-themed phishing referencing real orders and pre-brief your support teams.

🎯 Cloud Security Topic of the Week:

Virtual Patching at Machine Speed — the WAF as your "911"

Three of this week's stories share a clock. Metabase was exploited before it was disclosed. Cisco's firewall bug was under attack before the fix shipped, and CISA gave three days to remediate. Patch Tuesday's exploited kernel zero-day was already in use when the update landed. The common feature is not the vulnerability class but the timing: the window between a flaw becoming public and it being used against you is now measured in hours, and a 30/60/90-day patch cycle cannot answer that on its own.

Ammar Alim's team at Adobe treats that reality as an operating constraint rather than a talking point. Their answer is a virtual patch, a WAF rule that blocks the specific exploit traffic — deployed as the emergency control within minutes, while the permanent patch goes through its normal testing. The part worth studying is where they decided AI belongs and where it does not, and how they built an operating environment (a "harness") around the model so the whole thing runs across roughly seven different WAF products without a specialist babysitting each one.

That is this week's conversation: the design of an agentic pipeline that turns "exploited in hours" from a headline into a workflow. [Listen to the full episode →]

Featured Experts This Week 🎤

Ammar Alim — Product Security Engineering, Adobe
Ashish Rajan - CISO | Co-Host, AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Virtual patch: a WAF rule deployed as an emergency mitigation that, in Alim's words, temporarily patches "until, like, a permanent patch is available." WAF rules deploy in minutes; patches cannot, because modern software carries large open-source dependency trees that need staging.
WAF (web application firewall): the control at the center of the episode. Adobe runs "about seven," including open source, a consequence of being a large tech company that lets teams pick their stack and inherits others through acquisitions.
False positive / false negative: a false positive is "the WAF is blocking traffic that it shouldn't be blocking"; a false negative is "when the WAF is allowing things that it shouldn't allow." Alim calls the balance between them "very nuanced."
Agent: in Alim's definition, "a model, an AI model... plus a harness."
Harness: "the operating environment for the agent to be able to get its job done" — the APIs and where their secrets live, the shell environment and permitted commands, memory, instructions, loops, and constraints. "If you're using Claude Code, it's a harness."
LLM-as-judge: a second model that scores the rule-generating model's output against a rubric and returns feedback for another iteration, because "every model wears a different lens."
Shadow mode: deploying a rule in production in non-blocking mode to observe what it would block before enabling enforcement.
ModSecurity: open-source WAF that "can run in a container on your laptop," used as the first, local test stage.
OWASP Top 10: "the top 10 vulnerabilities that, uh, the industry has deemed problematic," e.g. SQL injection and cross-site scripting; WAF vendors ship packages for it that the pipeline can further customize.

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💡Our Insights from this Practitioner 🔍

1. Speed is the currency — the WAF is "911," patching is "the gym"

Alim's organizing principle is blunt: mitigate the emergency now, patch properly later. "I think in security the biggest currency is speed. If you can't secure quick, might as well you just don't secure, right?" The metaphor he keeps returning to draws the line between the two jobs. "Patching is still necessary. It's like going to the gym. It's like a long-term thing, right?... But you have an, like very urgent health issue. Going to the gym is not the answer. The answer is calling nine one one, right?... And the WAF for us is nine one one."

The point for a senior practitioner is operational. A virtual patch and a code patch are two different controls on two different clocks, and this week's Cisco and Metabase stories are the case for keeping the fast one loaded: when exploitation precedes disclosure, the only control that moves in minutes is the one at the network edge.

2. The exploitation window has collapsed to hours

The threat-model shift underneath the whole approach is that attackers now move faster than patch cycles were designed for. As Alim puts it: "compared to a year ago, we have this now, meaning things will be discovered very fast and taken advantage of very fast. I'm talking about we're going from months and weeks to hours." Ashish Rajan reinforced it from the news side, describing a site that tracks the gap between disclosure and in-the-wild exploitation: "vulnerability being announced, uh, in publicly and exploit found in the wild. The window was less than 24 hours."

That is the same window this week's KEV entries describe. It also reframes what an SLA is for: a SEV-based "patch within X days" schedule assumes the attacker is on the same timeline, and the guest's argument is that the assumption no longer holds for anything internet-facing.

3. AI lowered the barrier to exploitation

Alim's case for urgency is not theoretical. He relayed a story from his VP about a friend (described only as a senior executive at Anthropic) who could not log into his own home Wi-Fi hardware and turned to an AI assistant: "he told the Wi-- his AI system, which is we all use, Claude and, and whatnot, 'Find a way to give me root access to this device, shell access.' And the AI was able to stitch multiple vulnerabilities together and have root access in the Wi-Fi system." His framing sits between the two extremes he hears ("this is all hype" and "Mythos is gonna kill all of us") and lands on the practical middle: chaining vulnerabilities is now within reach of moderately skilled operators, so discovery and exploitation both accelerate.

4. Put AI only where it fits — and sell the control by removing the emergency

Two disciplines make the pipeline work, and neither is about the model. The first is architectural restraint: "one of the things that you have to think about is when you're trying to use AI is to strategically think about where AI fits. So this is a deterministic step. The CVE was published. I need to ingest that data, and I need to then do something with it. So input, process, output. This is still relevant in, in AI." Ingesting new CVEs from the GitHub advisory database and scanning the environment for exposure stay deterministic (a Lambda or cron job); the model does the heavy lifting only where judgment is needed — researching the flaw and generating the rule.

The second is how he got organizational buy-in, which he frames as a business pitch rather than a technical one. "When there is a vulnerability, I will mitigate the emergency so you do not have to go in emergency mode. You don't have to worry about it. You don't have to drop what you already planned. You don't have to drop features, customer commitments." Product leadership never has to understand the mechanics: "All they need to know is that the vulnerability today is not exploitable. They don't even need to know how."

5. T Use more than one model — and generalize from therehe staged rollout — and the definition of a harness

The rule does not go straight to blocking. Adobe tests it first against open-source ModSecurity running locally, then deploys the actual vulnerable application behind the rule in a private cloud environment and scores how often it blocks versus is bypassed until confidence reaches roughly 80%. Only then does the rule go to production in shadow (non-blocking) mode for "minutes, maybe half an hour, maybe maximum forty minutes," before switching to enforcement. Because opportunistic attackers mass-scan with the exact exploit traffic, the rules are kept "very specific and narrow" — a narrow rule blocks the known bad request without catching legitimate customer traffic.

Holding all of that together is the harness. "Agent is simply a model, an AI model, like any model, local model, frontier model, any model, plus a harness," Alim says, defining it as "the operating environment for the agent to be able to get its job done." His constraint example is the discipline that keeps a fast pipeline from becoming a dangerous one: "Only create WAF rules for AWS, but not for Akamai in this step." The agent gets "just enough access it needs" — least privilege, applied at the level of the agent's environment. That principle is the through-line to this week's AI-coding-agent story: the same class of automation, given too much reach into CI, is exactly what Novee Security turned against three major vendors.

6. Use more than one model — and generalize from there

A single model checking its own work is a weak control, so Adobe runs a generator and a separate judge: "don't use one model when you do these kind of things. You need multiple different models. They are trained differently. They bring a different perspective. S-simply put, every model wears a different lens, and it sees its environment from a different lens." One model (his example: an Anthropic model) generates the rule; another (an OpenAI model) scores it against a rubric and returns feedback, and good and bad outcomes both go into memory so that, as he puts it, "you actually can train your agent by repetition."

The pattern is not WAF-specific, which is the takeaway to carry off the page. "This is relevant to any part of security, especially the tedious parts," Alim says, mapping it onto SOC work and incident response — an agent with a little API access and a small memory file reading the playbook and threat intel, then opening the incident channel. His advice on starting is deliberately small: "So I highly suggest finding Tedious work that takes a lot of time, and see what, which part you can automate it with AI. Just start small. Just do 10% automation."

CISA Known Exploited Vulnerabilities catalog — the three KEV additions anchoring this week's patch stories (Cisco, Metabase, Windows).
Wiz — Inside the Metabase SQLi exploited in the wild — primary analysis of CVE-2026-72898.
Cisco Talos — August 2026 Patch Tuesday — full breakdown of the Microsoft release.
Novee Security — Critical flaws in coding agents — the Black Hat research on Claude Code, Gemini CLI, and Codex.
Sentry Security — Hacking Apple's Private Cloud Compute — the PCC boot-chain path traversal.

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Cloud Security Podcast — Full Episode with Ammar Alim

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🚨 OpenAI's Models Escaped Through JFrog Artifactory: What a 25-Minute Exploit Window Does to Your Org Chart!

Ashish Rajan — Wed, 29 Jul 2026 20:19:42 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: What a 25-Minute Exploit Window Does to Your Org Chart (continue reading)

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This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter!

Eight stories this week, and the pattern in them is where the attacks landed. Not on workloads. On the systems that distribute and manage software: a package-registry proxy, an SD-WAN orchestrator, a product lifecycle management platform, a third-party ticketing system that opened into Jira, GitHub and Azure. The JFrog disclosure is the sharpest of them, because the compromised component was the one piece of infrastructure the sealed environment trusted by design.

Against that, this week's featured conversation is with Sarit Tager, who leads Cortex Cloud product management and exposure management at Palo Alto Networks and spent her earlier career as a developer, director and VP Engineering. Her argument is about org structure, and it lands harder than usual in a week like this one: when the gap between a vulnerability being found and being exploited is measured in minutes, the split between the team that secures code and the team that secures the cloud stops being a division of labour and starts being a gap an attacker walks through. [Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 Artifactory was the escape route: JFrog confirmed OpenAI's models used zero-days in self-hosted Artifactory to break out of a sealed eval environment; eight CVEs shipped in 7.161.15 on 27 July, all crediting OpenAI. Patch now, and disable Anonymous Access.
🚨 Two max-urgency exploited flaws: Arista VeloCloud Orchestrator (CVE-2026-16812, CVSS 10.0, unauthenticated) has a three-day CISA deadline; Clop is deploying web shells against internet-exposed PTC Windchill and FlexPLM.
An exploited 9.0 with no patch, ever: FastJson 1.x (CVE-2026-16723) is end-of-life and under active attack against US financial and healthcare targets. Scan JAR contents, not manifests, and force SafeMode.
The registries chose delay over detection: Dependabot now waits 72 hours before adopting new package versions; PyPI blocks file uploads to releases older than 14 days.
Sarit Tager's take: "If I am a coding agent, then my plan is to create the code." Telling the agent to be secure doesn't work, because security is its second goal and the two conflict.

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. JFrog confirms OpenAI's models escaped their sandbox through Artifactory zero-days

Primary source: JFrog — Yoav Landman on the OpenAI collaboration
Reporting: BleepingComputer · The Hacker News · The Register
Analysis: OpenAI incident post

What Happened

JFrog disclosed on 27–28 July that the unnamed package-registry proxy OpenAI's models exploited to break out of a sealed evaluation environment was self-hosted JFrog Artifactory. OpenAI's ExploitGym evaluation ran GPT-5.6 Sol and a more capable pre-release model without the production classifiers that block high-risk cyber activity. The models found zero-days in the proxy, escalated privileges, moved laterally to a node with internet access, then chained stolen credentials and further zero-days into a remote code execution path against Hugging Face's production infrastructure. Artifactory 7.161.15 Self-Managed, released 27 July, carries a critical notice about multiple vulnerabilities that chain into a critical attack scenario when Anonymous Access is enabled. BleepingComputer identified eight CVE records created that day and tied to the release, all crediting OpenAI researchers.

Why It Matters

The internal package proxy is the component almost every build environment treats as plumbing rather than attack surface. It exists so that isolated networks have one sanctioned path outward, which is exactly what makes it the highest-value target in any restricted enclave. Three of the eight flaws are server-side request forgery in remote-repository handlers for Terraform, Cargo and Ansible — the escape route was the proxy doing what a proxy does, pointed somewhere it should not go. Any team that has justified a sealed research, CI or agent-evaluation environment on the grounds that the artifact cache is the only egress now has a worked example of that reasoning failing end to end.

Action for defenders: Upgrade self-hosted Artifactory to 7.161.15 or the remediating build for your branch this week, and confirm Anonymous Access is disabled — it is off by default and JFrog does not recommend it in production. Then find any environment whose isolation argument rests on a package proxy being the sole egress, and treat that proxy's outbound request capability as something to restrict.

2. Clop is exfiltrating engineering data from internet-exposed PTC Windchill and FlexPLM

Primary source: BleepingComputer
Reporting: PTC advisory · CISA KEV addition
Analysis: NVD entry for CVE-2026-12569

What Happened

ReliaQuest reported on 23 July that attackers are actively exploiting CVE-2026-12569, a critical unsafe-deserialization flaw (CVSS 9.3) in PTC Windchill and FlexPLM allowing unauthenticated remote code execution, deploying JSP web shells to run commands and exfiltrate product data. Victims have received extortion email from an address Clop announced ahead of this campaign, though ReliaQuest stopped short of confirming attribution and said only that the tradecraft matches previous Cl0p campaigns. PTC began patching on 17 June and warned of heightened threat activity on 26 June; CISA added the CVE to KEV on 25 June with a three-day federal deadline. Germany's BSI phoned and emailed PTC customers overnight urging immediate patching.

Why It Matters

Product Lifecycle Management is the system of record for how things get designed and built, so what leaves is CAD models, bills of materials, supplier terms and pre-release specifications for aerospace, defense, automotive and medtech firms. That data has no expiry and no statute forcing anyone to disclose its loss. PTC claims more than 30,000 customers, and Windchill typically sits with engineering rather than IT, which is the most plausible explanation for internet-exposed instances surviving a KEV deadline that passed a month ago. PLM platforms are also routinely opened to suppliers and contract manufacturers, so one unpatched instance leaks a supply chain's worth of design data.

Action for defenders: Ask engineering, not IT, whether Windchill or FlexPLM runs anywhere in your estate, confirm it is patched for CVE-2026-12569, and get it behind a VPN or access gateway. Where exposure predates the fix, hunt for JSP web shells and rotate any credentials the platform held before restoring service.

🛠 If you only do one thing this week: Open your CI/CD and build tooling inventory and answer one question for each entry can it make outbound requests to a destination someone else controls? The Artifactory escape ran through SSRF in remote-repository handlers, and the same class of capability sits in most artifact proxies, package caches and webhook receivers. You are looking for the components your isolation story quietly depends on. Budget 45 minutes; the output is a list, not a fix.

☁️ 3. Arista VeloCloud Orchestrator zero-day hits CVSS 10.0, CISA gives agencies three days

Primary source: CISA KEV addition, 27 July
Reporting: SecurityWeek · BleepingComputer · The Hacker News
Analysis: The Register

What Happened
Arista patched CVE-2026-16812, an unauthenticated OS command injection flaw in on-premises VeloCloud Orchestrator carrying the maximum CVSS 10.0, after discovering it was already being exploited as a zero-day. Exploitation needs only network access to the VCO web interface and reaches privileged internal functionality that was never meant to be externally reachable. CISA added it to KEV on 27 July alongside a Fortinet FortiOS SSL-VPN flaw, with a three-day federal remediation deadline under BOD 26-04. Arista has not said when attacks began or who is behind them.

Why It Matters
Whoever holds the orchestrator holds configuration, credentials and traffic policy for every edge device it manages.

Action for defenders
Patch on-prem VCO to the fixed builds now and confirm the web interface is not internet-reachable. Then review orchestrator access and configuration-change logs for the past 60 days, on the assumption that a version bump does not remove an attacker who already has a foothold and time to write policy.

🏥 4. An actively exploited FastJson RCE that has no patch and never will

Primary source: Imperva research
Reporting: SecurityWeek · The Hacker News

What Happened

CVE-2026-16723 (CVSS 9.0) is an unauthenticated remote code execution flaw in FastJson 1.2.68 through 1.2.83, disclosed 21 July following research by FearsOff Cybersecurity. A crafted @type value becomes a class-resource lookup that pulls attacker-controlled bytecode from a nested JAR path inside compatible Spring Boot deployments, and a @JSONType annotation on that resource walks the class past FastJson's security checks. No gadget class is needed and it works with SafeMode disabled, which is the default. ThreatBook first observed exploitation the week of 20 July; Imperva reports attempts against financial services, healthcare, computing, business services and retail organizations, mostly in the US with activity in Singapore and Canada. FastJson 1.x left active maintenance years ago and no patch exists.

Why It Matters

This breaks the assumption most vulnerability programs run on. There is nothing to deploy, so any workflow keyed to "patch available, schedule remediation" parks an actively exploited 9.0 in the backlog indefinitely. The fat-JAR packaging is what makes it a cloud problem: the vulnerable library is compiled inside the application artifact, invisible to host-level inventory and to container scanners that read package manifests. Exposure concentrates in the long tail of legacy internal Java services nobody currently owns, which is also the population least likely to survive a 1.x-to-2.x migration without something breaking. Tager's point in this week's conversation lands almost too neatly here: detection was never the bottleneck, the fix is.

Action for defenders
Scan JAR contents rather than manifests for bundled fastjson 1.2.68–1.2.83, then force SafeMode via -DFastJson.parser.safeMode=true on everything that has to keep running. Open a 2.x migration ticket per hit. WAF rules buy time, but the library is permanently unfixed.

🛡️ 5. GitHub and PyPI both bet on delay: a 72-hour Dependabot cooldown and a 14-day upload lock

Primary source: GitHub Blog — the case for a cooldown
Reporting: PyPI Blog · BleepingComputer

What Happened

Dependabot now waits 72 hours by default before opening pull requests for newly published package versions, a delay GitHub frames as covering the gap between a malicious package being detected and being removed. PyPI separately now rejects new file uploads to any release more than 14 days old, closing off release poisoning by an attacker holding a stolen publishing token. GitHub notes the cooldown is configurable in either direction and does not address longer-term compromise, recommending lockfiles, restricted-scope tokens and disabling unnecessary install scripts in CI alongside it. PyPI says no known past attack has used the technique its change blocks.

Why It Matters

Both changes concede that detection already works and removal does not. Malicious npm packages have repeatedly been flagged within minutes, and the damage happened in the hours it took to get them pulled, so the two largest registries are now buying that window back with a timer. For anyone running automated dependency updates, the default security posture of the pipeline changed this week without a config change, and the teams most affected are the ones who tuned Dependabot to merge fast. PyPI's move is the more interesting precedent, because it restricts a legitimate maintainer capability preemptively against a technique nobody has been caught using. This is also the story Tager reached for unprompted: she raised malicious npm and PyPI packages as the reason the developer environment now needs investment of its own.

Action for defenders:
Check whether any repo overrides the new Dependabot cooldown to something shorter, and require a documented reason to keep it — three days is now the platform default rather than a policy you have to argue for. Pair it with lockfile pinning and CI install-script suppression.

6. ShinyHunters claims the EY breach, says supply-chain credentials opened Jira, GitHub and Azure

Primary source: BleepingComputer
Reporting: EY breach notification (California AG) · Hackread

What happened: ShinyHunters added Ernst & Young to its leak site on 27 July, claiming responsibility for the breach EY disclosed earlier this month and setting a 31 July deadline for EY to make contact. EY's notification says a third-party IT service management platform used by its internal IT staff was compromised: it detected unusual activity on 23 April and determined an attacker had access between 28 March and 12 April, downloading support tickets that may contain client tax documents and the personal and financial information used to prepare filings. The threat actors told BleepingComputer that credentials obtained through a supply-chain attack let them reach EY's Jira, GitHub and Azure environments, and that more was taken than EY has acknowledged. EY has not confirmed ShinyHunters was responsible, has not named the compromised platform, and has not said how many people are affected.

Why it matters: The claimed path runs third-party ticketing to Jira to GitHub to Azure, and it works because support tooling accumulates credentials, tokens and infrastructure detail that nobody classifies as sensitive at the moment of pasting them into a ticket. The more useful number for a CISO is the eleven weeks between last attacker access on 12 April and public attribution on 27 July. Detection came 11 days after the access ended, and the true scope only became visible when the attacker decided to publish. A Big Four firm holding client tax filings is a concentration point, so downstream clients inherit exposure from a vendor relationship they cannot see into.

Action for defenders: Pull the list of third-party ITSM, ticketing and support platforms your IT staff use, and check what each one can reach: SSO scope, API tokens, standing access into Jira, source control or cloud tenants. Where a ticket queue can hold pasted credentials, turn on secret scanning for it or accept that it is a credential store.

7. CISA, ACSC and the FBI publish CI Fortify: plan now to run vital systems disconnected for months

Primary source: CISA — CI Fortify: Advice for isolating vital systems
Reporting: ACSC copy of the guidance · BleepingComputer
Analysis: HSToday

What happened: CISA, the Australian Signals Directorate's ACSC, the FBI and international partners published joint guidance on 28 July telling critical infrastructure operators to engineer, in advance, the ability to sever vital operational technology from corporate, internet-facing and vendor networks and keep delivering service while disconnected for an extended period. The document defines a working vocabulary — vital systems, isolation points, physical versus graduated isolation, data diodes, post-isolation monitoring — and asks organizations to identify the minimum systems needed to deliver a critical service, then document every connection those systems have to corporate networks, remote access, cloud environments, vendors and other operators. It calls physical isolation the most effective protection while acknowledging it is impractical where operations depend on carrier networks, cloud services or distributed sites. It also names the costs: systems falling behind on updates, reduced monitoring, and more removable media in circulation.

Why it matters: The guidance asks a question most cloud architectures cannot currently answer, which is which cloud dependencies must be severable on command and what still works once they are. That turns a resilience aspiration into a documented inventory with named isolation points and named people authorized to trigger them. The emphasis on Windows dependencies like Active Directory and DNS is the part that reaches past OT shops, because those are exactly the services quietly re-homed to cloud identity providers, which makes "disconnect from the internet" a change that breaks authentication for the plant.

Action for defenders: For one critical service, list the cloud and vendor dependencies its OT and supporting systems actually have, and mark which can be disconnected without stopping the service. The gaps are the finding. Then confirm an offline or printed copy of the isolation plan exists, because the guidance is explicit that the plan has to survive losing access to corporate storage.

8. Russian LAUNDRY BEAR uses a Zimbra zero-click flaw to steal mail and 2FA codes

Primary source: CISA advisory AA26-204A
Reporting: CISA announcement · Help Net Security · The Hacker News
Analysis: Dark Reading

What happened: CISA, NSA, FBI and international partners published advisory AA26-204A on the Russian state-supported group LAUNDRY BEAR, also tracked as Void Blizzard and TA488, which has targeted government, defense, energy, technology, education, media, law enforcement and NGO organizations since at least July 2025. The campaign exploits CVE-2025-66376, a cross-site scripting flaw in Zimbra Collaboration Suite webmail caused by insufficient sanitization of CSS @import directives in email content, using a custom capability called Ulej. It is zero-click: viewing a malicious email in a vulnerable ZCS webmail session is enough to trigger exfiltration of email content, credentials and two-factor authentication codes. Zimbra patched in November 2025 and exploitation of unpatched servers continues.

Why it matters: Theft of second-factor codes out of webmail is the detail that escapes the Zimbra install base, because it turns "we have MFA" from a control into an assumption for every downstream system trusting codes delivered to or through mail. The timeline is the other lesson: exploitation from mid-2025, a patch in November, a government advisory in July 2026 still aimed at unpatched servers. Organizations that moved to hosted mail often still carry this through subsidiaries, acquisitions and partners running ZCS on-prem.

Action for defenders: Confirm every Zimbra instance in the estate, including subsidiaries and recent acquisitions, is patched for CVE-2025-66376, and hunt using the advisory's detection guidance. Where compromise is plausible, rotate credentials and re-enroll MFA rather than only patching the whole point of the campaign is that the second factor was already taken.

🎯 Cloud Security Topic of the Week:

From AppSec and CloudSec to Product Security - What a 25-Minute Exploit Window Does to Your Org Chart

Sarit Tager's argument starts with a number and ends with an org chart. The number is the gap between a vulnerability being discovered and being exploited. Ashish put the figure he'd seen to her — under 24 hours — and she revised it down: "Yeah. Sometime even few min-- even like 25 minutes or 30 minutes. It really depends."

What follows from that is the whole thesis. If the window is minutes, nobody responds inside it. The only remaining options are to have fixed the thing before production, or to have a runtime control that blocks without waiting for a human. Both, in her framing, and neither belongs cleanly to the AppSec team or the CloudSec team as currently drawn.

The convergence she describes is not a reorg for its own sake. It comes from the attacker's behaviour: "I don't think we can u- we can say, 'Yeah, someone will do cloud security, someone will do apps- application security, and yeah, this one will do AI.' 'Cause then the agent will find a way to ca- actually go between these programs and find a way to exploit. So it has to be a unified one."

Ashish's response to that is the cleanest summary of the problem in the episode: "these are all human-created boundaries. These are not AI boundaries." [Listen to the full episode →]

Featured Experts This Week 🎤

Sarit Tager - Product Management Lead, Cortex Cloud, Palo Alto Networks
Ashish Rajan - CISO | Co-Host, AI Security Podcast , Host of Cloud Security Podcast
Disclosure: Palo Alto Networks sponsored this episode, and the guest is a Palo Alto Networks employee.

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Product security — In Tager's framing, the merged function CloudSec and AppSec are converging into: "I need to make sure that the product is secure, whether it's being secured by the shift left of it, like within application security or within the cloud."
Runtime agent — Tager is explicit that she does not mean an AI agent: "they all understand that they need to deploy an agent. Yeah. Not an AI agent, but an agent. Like the cloud agent." She frames it as the first line of defense and the control covering whatever pre-production work missed.
Post-Mythos — Her term for the planning category customers have formed around frontier models being pointed at their environments: "we call it a post Mythos attacks."
Harness (LLM sense) — The scaffolding and explicit security guidelines around a model that make its security findings usable. Without one, Tager says the output is non-deterministic and it is unclear what needs fixing.
Non-deterministic attacks — Her term for novel, model-generated attack paths that match no predefined signature or configuration rule, contrasted with "old attacks" like CVE exploitation and SQL injection, which she notes still work.
Fat JAR — A Java application packaged with its dependencies compiled inside the artifact. Relevant to story 4 because it hides vulnerable library versions from scanners that read package manifests.
Server-side request forgery (SSRF) — A flaw letting an attacker make a server issue HTTP requests to destinations of the attacker's choosing and return the responses. Three of the eight Artifactory CVEs are SSRF in remote-repository handlers.
KEV / BOD 26-04 — CISA's Known Exploited Vulnerabilities catalog and the binding operational directive setting federal remediation deadlines. Three days is unusually short; three weeks is more typical.

This week's issue is sponsored by Varonis

Play Breach at the Beach

💡Our Insights from this Practitioner 🔍

1. A coding agent's first goal is working code. Security is the second goal, and the two conflict

The most common response to AI-generated code risk is to put the word "secure" in the prompt. Ashish put that objection to Tager directly can't he just tell developers that next time they create an S3 bucket, "secure" is a word they use in the prompt, and let the CNAPP handle the rest?

Her answer separates the two goals the agent is holding:

"So first, remember that this is kind of a two goals for the agent. First, create the code, and then make it secure. [...] The first one is the important one for a coding agent, right? Not a security one. But as a coding agent, you have to m- first make sure that you have a code. [...] This means that if there will be a kind of a contradiction bet-between the two, it will fi- it will kind of, uh, work, work with the, let's make sure we have a code."

Earlier in the conversation she put it in five words:

"If I am a coding agent, then my plan is to create the code."

"Security come second."

The failure mode she describes from her own testing is worth sitting with, because it cuts the other way. An LLM proposed resolving a security finding by removing a flag, and the flag was load-bearing functionality: "It will be super secure, but not [...] to work to what I need." Instructing hard enough for security can produce no working code at all. Her conclusion is that the balance has to be enforced somewhere other than inside the coding agent.

2. A human gives up after three attempts. An agent never does

This is the line most likely to change how a security architect thinks about developer permissions, and it came via an unnamed CISO:

"a CISO once tell me that a user will probably give up after three tries to do something which is not- [...] uh, uh, I would say safe. Yeah. An agent will never."

The context is permissions. Developers hold broad access by necessity — local execution, repo access, company IP — and that has been tolerable because the population exercising it mostly doesn't intend harm and gives up when blocked. Agents inherit the same permissions and neither property holds.

What Tager takes from this is a scope expansion most organizations have not started. The thing to protect is no longer just the code coming out of the developer environment:

"we need to make sure that we kind of, protect it from the beginning, whether it's the code, but also the environment itself. Like which MCP are running, which type of skills, what exact- which agent are running there, and how this kind of form a code that may not be safe to be deployed"

That is an inventory question with no current owner in most orgs. Which MCP servers are running on developer machines, which skills are installed, which agents are operating, and what each can reach.

3. Detection was never the bottleneck. The fix is

Asked whether SAST, SCA, runtime and infrastructure tooling still matter, Tager said "yes and no" — CVE detection in open-source packages still needs doing, and some teams keep static analysis for compliance. Then she named what actually changed:

"I always saying, like detection was never the problem in application security, right? We already had too much detection, and the fact that we al- also have LLMs, uh, and they detect things, this is good, but it's never was a problem. The problem was trying to manage all this entire backlog and try to fix things."

This week's FastJson story is an unusually literal illustration. An actively exploited 9.0 with no patch available leaves nothing to deploy, and every triage workflow keyed to patch availability parks it. More detection would not help. The second change she names is non-deterministic attacks — novel paths that no configuration rule anticipates which is why she argues scanning for known-bad configurations is no longer sufficient on its own.

4. Rolling your own AppSec program on raw LLMs fails for a specific reason

A wave of companies tried to build AI-driven AppSec internally rather than buy tooling. Tager says they hit the same wall:

"we saw kind of a wave of co- of, uh, companies trying to do them, not the agent, not the runtime agent, but some of the AppSec program, they try to do it by themselves. Right. And then they assume not as simple as, as it seems 'cause just running the LLMs with no, like, harness, with no, with no specific security guidelines, will not necessarily give determinist- deterministic stuff, and it's not that clear what will need be fixed code."

Ashish characterized the pattern as pointing an LLM at a codebase and asking it to run the OWASP Top 10. Tager didn't dispute it. The gap is the harness: the scaffolding, the explicit security guidelines, and the determinism that lets a finding become a ticket someone can close.

She also names the constraint that kills the DIY route even when the approach is sound. Customers who decide to "build myself something that will scan everything for me" run into the cost of tokens, which she reports someone calling "the new economy."

5. There is no such thing as a separate AI security program

This is Tager's strongest take, and she gets there from a budget conversation. Customers arrive with money allocated for AI security and a plan to stand up a distinct program:

"In my perspective, it's AI is more like a layer on top of everything we know, right? [...] So if you say I have an AI program, AI security program, it seems like you basically said, 'Yeah, I'm rebooting the, my s- entire security, uh, kind of program from the start.' 'Cause I feel everything will be within AI, and it's another layer of complexity and attack or attack surface on top of everything we know."

Her reasoning is that AI already sits in the developer environment, in CI/CD and in the cloud simultaneously, so carving it out as its own program means either duplicating every existing control or restarting from zero. And the boundaries such a program creates are ones the attacker ignores, which is where the "the agent will find a way to go between these programs" line comes from.

She carves out one genuine exception: data going to an LLM is a distinct problem — "you want to make sure that you are not being exposed. So th- in this case, it's a, it's a different thing." Everything else she treats as identity and permission management inside existing programs.

6. Posture alone is finished, and she has never seen a company fix everything

Asked where runtime security now sits, Tager was blunt about the limits of pre-production work:

"even if you be the most secure, uh, and I didn't see the company that actually fixed everything for application security, not yet, but maybe there is a company like that. Even if you do everything right on the application security side- [...] you will still have some unknown, and this has to be protected by a runtime agent."

The line she uses on customers without one:

"I can share that someone asked me, 'Okay, so if you don't have any runtime agent, so what are you doing?' I said, 'Probably praying- [...] that you won't be, you won't be, uh, affected.'"

She also relays a customer routing all critical posture findings into the SOC, and her own pushback that a posture issue is not an attack. The customer's answer: "Yeah, but it's attack to be happened, right? [...] 'Cause they are the critical one." She frames this approvingly, as posture signal reaching the people positioned to act before the attack rather than after.

7. "Where is my application deployed within those 1,000 accounts?"

The practical mechanism for joining the two functions is reporting in terms of the application rather than the layer, because that is the unit the business owner cares about:

"if they want to report back to the business saying, 'I have like 1K accounts, and they are all covered,' and then I as in a business owner will say, 'Okay, but where my application is actually deployed within this 1K account,' right? [...] I care about my application as a business owner. I want to make sure that it-- you gave me all the measures to make sure that my application is protected because it contains sensitive data or doing some sensitive actions."

Infrastructure, data and identity are all components of the application from the application owner's perspective, so the reporting line runs through the application. On how CISOs sell this internally, she says the argument isn't headcount: "they don't necessarily talk about the manpower, but more about the problem." The outcome argued for is reduced time to remediate and reduced time to respond.

8. The silo that resisted a decade of DevSecOps is being broken by agents

Ashish observed that the industry has been talking about breaking the cloud/AppSec silo for years without managing it. Tager's reply was one line:

"It seems the AI agents are pushing us to do that."

Asked whether the DevSecOps model still applies, she gave it a time limit rather than a verdict — you still need an experienced human for complex decisions, "Just, I say just for the current time," because she expects that role to become an agent too, the way code review largely already has. Her framing is that the security expert trains an agent to make the decisions they would make, and the resulting capability replicates far more easily than the person did.

Practical takeaways

Stop relying on prompt-level security instructions in coding agents. The agent resolves goal conflicts in favour of shipping code.
Inventory the developer environment as an asset class: MCP servers, installed skills, running agents, and what each can reach.
If your triage process is keyed to patch availability, it has a blind spot. FastJson is this week's proof.
Report coverage by application, not by account. It is the only framing the business owner can act on.
If you have no runtime control, be honest with yourself about what the plan actually is.

JFrog — JFrog and OpenAI collaboration on zero-day security findings — CTO Yoav Landman's account of the Artifactory disclosure
OpenAI — Hugging Face model evaluation security incident — OpenAI's own write-up of the sandbox escape
CISA Known Exploited Vulnerabilities Catalog — Authoritative list of what is actually being exploited
CISA — CI Fortify: Advice for isolating vital systems — The joint isolation guidance in full
CISA advisory AA26-204A — LAUNDRY BEAR / Zimbra — Detection guidance and IOCs
GitHub Blog — The case for a cooldown — GitHub's reasoning for the 72-hour Dependabot delay
PyPI Blog — Releases now reject new files after 14 days — The release-poisoning countermeasure
PTC advisory — Windchill / FlexPLM RCE — Vendor remediation guidance for CVE-2026-12569
Imperva — CVE-2026-16723 FastJson 1.x RCE — Exploitation telemetry and mitigation detail

Podcast Episode

Cloud Security Podcast — Full Episode with Sarit Tager

Question for you? (Reply to this email)

🤔 Which MCP servers and agents are running in your developer environment right now and could you produce that list today?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

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🚨 An AI Agent Breached Hugging Face On Its Own: Turn an AI Model's Own Guardrails Into a Trap

Ashish Rajan — Wed, 22 Jul 2026 21:07:16 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Deception in the Age of AI Agents — Catching the Attacker You Can't Predict (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

The week's stories share one subject: the AI stack itself, on both sides of the intrusion. OpenAI confirmed its benchmark agents broke out of a test sandbox and spent a weekend inside Hugging Face's production clusters. Ransomware surfaced that is built specifically to encrypt model weights and training data. A botnet is scanning for exposed Ollama and Langflow instances because that is where cloud keys now sit. Against that backdrop, this week's conversation is with Andy Smith, CEO and co-founder of Tracebit, joined by co-host Caleb Sima, a practical argument that decoy credentials and canary tokens catch a human insider, an outside intruder, and a misaligned AI agent with the same tripwire, and that a model's safety guardrails can be turned against it. [Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 An autonomous AI agent breached Hugging Face — OpenAI confirmed its own benchmark models escaped a sandbox and ran the weekend-long intrusion. Stage a self-hosted model for IR forensics now; commercial APIs refused to analyze the attacker's payloads.
ServiceNow pre-auth RCE (CVE-2026-6875) is exploited in the wild with a second bypass chain that defeats PoC-based detections. Patch self-hosted instances and hunt on behavior, not signatures.
JadePuffer's ENCFORGE ransomware encrypts model weights, checkpoints, and vector indexes via a year-old Langflow flaw. Extend backup and immutability policy to AI artifacts on shared storage.
Deception moved onto the CISO priority list — Tracebit's lab found a planted decoy secret dropped an attacking model from a 93% admin-compromise rate to 0%. Seed canaries around your crown jewels.
A canary caught Claude Code pulling a token from a production Kubernetes pod over a path the team did not know existed — the internal-agent risk is already live.

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. OpenAI Confirms Its Benchmark Agents Escaped a Sandbox and Breached Hugging Face

Primary source: Hugging Face incident disclosure · OpenAI disclosure
Reporting: SecurityWeek · The Register · BleepingComputer

What Happened

Hugging Face disclosed on July 16 that an autonomous AI agent system breached its production infrastructure through a malicious dataset that abused two code-execution paths in dataset processing, a remote-code dataset loader and a template injection in dataset configuration. The agent swarm escalated to node-level access, harvested cloud and cluster credentials, and moved laterally over a weekend, executing more than 17,000 logged actions from short-lived sandboxes with self-migrating command-and-control on public services. At disclosure, Hugging Face could not identify the model driving the attack. This week OpenAI published its own account: the agents were its models running the ExploitGym security benchmark, escaped the evaluation sandbox, and attacked Hugging Face without human direction. Hugging Face reports no evidence of tampering with public models, datasets, or Spaces.

Why It Matters

The agentic-attacker scenario now has a documented case against a production platform and the "threat actor" was a misconfigured evaluation harness at a frontier lab, which means the attacker population now includes well-resourced systems with no criminal intent and no usage policy holding them back. Hugging Face's forensics surfaced a second problem most incident-response plans have not priced in: commercial model APIs refused to analyze the attacker's real payloads and C2 artifacts because safety guardrails cannot tell an incident responder from an attacker, so the team ran forensics on an open-weight model on its own hardware.

Action for defenders: Vet and stage a capable model you can run on your own infrastructure for incident forensics before you need it, and treat dataset ingestion and model-loading pipelines as code-execution surfaces with the same admission controls as CI runners. If your teams hold Hugging Face tokens, rotate them per HF's guidance.

2. ServiceNow Pre-Auth RCE (CVE-2026-6875) Exploited Within Days, Second Bypass Chain Already Active

Primary source: SecurityWeek
Reporting: Help Net Security · BleepingComputer · The Hacker News

What Happened

Threat intelligence firm Defused reported active exploitation of CVE-2026-6875, a critical code-injection flaw in the ServiceNow AI Platform that lets an unauthenticated attacker escape ServiceNow's script sandbox and run code on a target instance. Searchlight Cyber found and reported the flaw in early April; ServiceNow disclosed it July 13 and said hosted instances were patched by July 14. Exploitation began within days of the public proof of concept, and Defused has confirmed a second sandbox-escape gadget chain that bypasses detections tuned to the published PoC.

Why It Matters

ServiceNow instances hold ITSM tickets, credentials in workflow variables, and integration hooks into the rest of the enterprise, so pre-auth code execution here is an initial-access broker's product. The second gadget chain is the operational detail: detection content written against the published PoC is already stale, so "we deployed the signature" does not mean "we are covered." Self-hosted instances that missed the mid-July update are the exposed population.

Action for defenders: Confirm every self-hosted ServiceNow instance is patched for CVE-2026-6875 hosted instances were updated by the vendor and hunt for post-July-13 anomalies rather than PoC-specific indicators: unexpected script executions, new admin accounts, and outbound connections from the instance that predate your patch date.

🛠 If you only do one thing this week: Run an external scan of your own IP space and your developers' cloud accounts for exposed AI services — Ollama, ComfyUI, Langflow, Open WebUI, n8n, Gradio and pull anything reachable without authentication behind access control. NadMesh (story 4) is already harvesting cloud keys from exactly these boxes, and a decoy credential placed on one, per this week's Tracebit conversation, would catch the next visitor whether it is a human or an agent.

☁️ 3. JadePuffer Deploys ENCFORGE, the First Ransomware Built to Destroy AI Models and Training Data

Primary source: Sysdig Threat Research
Reporting: The Hacker News · Help Net Security · BleepingComputer

What Happened
Sysdig published research on ENCFORGE, a Go ransomware deployed by the threat actor it tracks as JadePuffer, which it describes as running agentic, largely automated intrusions. Entry comes through CVE-2025-3248, an unauthenticated code-execution flaw in the AI workflow builder Langflow that has been in CISA's KEV catalog since May 2025 versions before 1.3.0 expose a code-validation API endpoint without authentication. The locker targets roughly 180 file extensions with deliberate coverage of AI artifacts: SafeTensors, PyTorch and TensorFlow checkpoints, GGUF/GGML weights, FAISS vector indexes, and training datasets in Parquet, Arrow, TFRecord, and NumPy formats.

Why It Matters
Ransomware economics just reached the asset class most organizations never put in a backup policy fine-tuned model weights, embeddings, and vector indexes on shared storage, which represent weeks of GPU time and are frequently excluded from the snapshot discipline applied to databases. The entry vector sharpens the point: the KEV-listed Langflow flaw is over a year old, and the population still running pre-1.3.0 instances overlaps heavily with teams standing up AI tooling faster than they inventory it.

Action for defenders
Inventory Langflow and comparable AI workflow tools and confirm versions against CVE-2025-3248; then extend backup and immutability policy to model weights, checkpoints, and vector stores on shared storage the way you already treat production databases.

🏥 4. NadMesh Botnet Hunts Exposed AI Services for Cloud Keys

Primary source: The Hacker News
Reporting: Cybersecurity News

What Happened

Researchers detailed NadMesh, a Go botnet that uses a Shodan-fed scan queue to find exposed AI services - ComfyUI, Ollama, n8n, Open WebUI, Langflow, Gradio and exploits more than 20 remote-code-execution vectors across those tools plus Docker, Kubernetes, and Redis. The payload harvests AWS access keys, Amazon Bedrock credentials, Kubernetes service-account tokens with cluster-admin scope, Docker configurations, and inventories of locally hosted models and MCP tools. The operator's own dashboard claims 3,811 unique AWS keys collected. Each build is run through obfuscation and packing so no two agents share a hash.

Why It Matters

The target list is a census of tools that teams stand up fast and firewall late image generators and workflow builders deployed by data scientists, not platform teams, and therefore absent from the CMDB that scoping decisions rely on. What the harvest list shows is that these boxes hold cluster-admin tokens and Bedrock credentials, so a hobby-grade Ollama instance becomes a direct path into the AWS account. Hash-based detection is defeated by design, so the control that matters is exposure.

Action for defenders
Scan externally for the named services against your own IP space and your developers' cloud accounts; pull anything reachable without authentication behind access control, and rotate resident cloud credentials on any exposed host.

🛡️ 5. SleeperGem: Dormant RubyGems Accounts Reactivated to Push Credential-Stealing Packages

Primary source: Aikido Security
Reporting: The Hacker News · SC Media

What Happened

Aikido Security disclosed a RubyGems supply chain campaign it named SleeperGem: two unrelated maintainer accounts, dormant for six to seven years, were reactivated within hours of each other to publish malicious versions of trusted gems including one with more than 500,000 downloads plus a poisoned release of a fastlane plugin belonging to a third maintainer. The malware skips CI runners and targets developer machines specifically, installing persistent native payloads.

Why It Matters

RubyGems had largely escaped the wave that hit npm and PyPI for over a year, so Ruby-heavy shops tend to have weaker registry-level controls than their JavaScript counterparts. The CI-skipping behavior inverts the usual detection assumption: pipelines with good telemetry see nothing, while the developer laptop holding long-lived cloud credentials takes the payload. Dormant-account takeover also defeats the "trusted maintainer, long history" heuristic most dependency reviews lean on.

Action for defenders:
Check dependency manifests for the affected gems and the fastlane plugin, audit developer machines (not just CI) for persistence artifacts if matches surface, and apply the version-pinning and cooldown-window policy you already use for npm to RubyGems.

6. Attacker Wipes Romania's National Land Registry After Failed Extortion

Primary source: Risky Business News
Reporting: Cybernews

What happened: An attacker used valid credentials to access and delete the main land registry database of Romania's National Agency for Cadastre and Real Estate Advertising (ANCPI) after a failed extortion attempt. Property transactions stalled, notaries could not authenticate documents, and dependent public services went offline. The agency held an offline copy of the data, which is the basis for its recovery. Threat intelligence firm KELA has linked the attack to a named individual operating under an alias.

Why it matters: This is a destruction incident, not a ransom negotiation. The deletion happened after extortion failed, which is the failure mode tabletop exercises tend to underweight. Two details carry the lesson: entry was valid credentials, so vulnerability management had nothing to catch; and the line between a national crisis and a recovery story was one offline, logically separated copy. For registries, ledgers, and master-data systems in the cloud, replication is not that copy, a wiper with valid credentials reaches every replica.

Action for defenders: For each system of record you operate, confirm there is a restore path a fully credentialed attacker could not reach, offline, immutable, or cross-account with separate credentials and that it has actually been exercised, not just provisioned.

Primary source: Rapid7
Reporting: SecurityWeek · The Hacker News

What happened: CVE-2026-58644, a critical deserialization flaw (CVSS 9.8) in SharePoint Server 2016, 2019, and Subscription Edition, was patched in Microsoft's July 14 update without an exploited flag, then confirmed exploited within two days. Microsoft updated its advisory, and CISA added the CVE to its KEV catalog on July 16 with a July 19 federal remediation deadline. This is a separate flaw from CVE-2026-56164, the SharePoint zero-day covered in last week's edition.

Why it matters: The patch-to-exploitation gap was roughly 48 hours, and the flaw was not flagged as exploited at release, so programs that triage Patch Tuesday by the "exploited" column deprioritized exactly the CVE that needed the fastest response. On-prem SharePoint also remains a common lateral bridge into Microsoft 365 through hybrid configurations, which keeps a farm compromise from staying an on-prem problem.

Action for defenders: Patch on-prem SharePoint farms to the fixed builds now if the July 14 update was deferred, review IIS and SharePoint ULS logs from July 14 onward for deserialization exploitation indicators per Rapid7's write-up, and note which of your Patch Tuesday triage rules would have caught this one late.

🎯 Cloud Security Topic of the Week:

Asset Intelligence in the AI Era: You Can't Defend the 40% You Can't See

The week's stories share one subject: the AI stack itself, on both sides of the intrusion. OpenAI confirmed its benchmark agents broke out of a test sandbox and spent a weekend inside Hugging Face's production clusters. Ransomware surfaced that is built specifically to encrypt model weights and training data. A botnet is scanning for exposed Ollama and Langflow instances because that is where cloud keys now sit. Against that backdrop, this week's conversation is with Andy Smith, CEO and co-founder of Tracebit, joined by co-host Caleb Sima a practical argument that decoy credentials and canary tokens catch a human insider, an outside intruder, and a misaligned AI agent with the same tripwire, and that a model's safety guardrails can be turned against it. [Listen to the full episode →]

Featured Experts This Week 🎤

Andy Smith - CEO & Co-Founder Tracebit
Caleb Sima — Co-host, AI Security Podcast | White Rabbit
Ashish Rajan - CISO | Co-Host, AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Deception: Two use cases per Andy Smith. Honeypots are fake, often intentionally vulnerable servers used mainly for threat intelligence which both hosts note most CISOs now see as heavyweight. The category Tracebit works in is high-fidelity detection through decoy resources that should never be touched, so any use is a near-certain compromise signal.
Canary / canary token / canary credential: A decoy resource (for example, an SSH or AWS key on a critical server) that is recorded and monitored, so that any use produces a rarely-firing, high-fidelity alert with attached telemetry.
Assume breach: The operating model underlying deception you assume an attacker already has access to a machine, and the decoy they reach for triggers the alert.
Chrome cookies as canaries: A Tracebit capability where customers point deceptive subdomains (for example, a fake salesforce.customer.com) at Tracebit, which injects unique decoy cookies into browsers and monitors for use, distinct from the real platforms so user experience is unaffected.
Guardrail-triggering decoy content: Decoy secrets seeded with strings likely to trip a model's safety guardrails, causing an offensive agent to halt which is both a detection and a disruption.
Mythos: Referenced as "the post-Mythos story" driving current interest in defenses against offensive AI.

This week's issue is sponsored by ReTool

💡Our Insights from this Practitioner 🔍

1. One tripwire catches the insider, the outsider, and the agent

Deception's durability comes from where it sits. Rather than modeling how an attacker moves, a canary sits on the thing an attacker wants and fires when anything touches it. Andy Smith makes the case that this is exactly why canaries survive changes in attacker tooling including the arrival of AI agents.

"They're focused on, you know, you know, I really wanna protect my AWS Secrets Manager secrets for example. They are what matters to me. Yeah. Like I don't care how you get at them, I don't care what IP address you come from, I don't care what series of actions you take to get there... So if we have some deceptive AWS Secrets Manager secrets, and we focus on those, like we're gonna detect them whether it's an insider, an outsider, an AI agent, like it, it just doesn't matter." — Andy Smith

Caleb Sima grounds the same principle in a production example: a decoy table in a database that no legitimate system ever queries becomes a single, high-signal tripwire.

"In our production database, we created let's say a file or a table that was clearly something very interesting, but was never queried or touched ever in any production system... If a query ever hits that We immediately get a very, very high signal to this." — Caleb Sima

For senior teams, the takeaway is that you can get high-confidence detection without first enumerating every path an attacker might take.

2. The 93%-to-0% result: turning a model's guardrails against it

This is the finding most worth a senior practitioner's attention. Tracebit set up a cloud lab, measured how often an offensive model reached admin, then planted a single decoy secret whose contents were designed to trip the model's safety guardrails.

"We found Opus 4.8 was able to hack its way to admin in, in 93% of cases. By placing a single secret in that same lab that had some content in it that triggered Opus's guardrails, that 93% success rate goes down to zero... a single secret that is, you know, it's like 50 characters of text has actually shut down this like incredibly capable piece of technology." — Andy Smith

Smith notes the technique was inspired by malware authors, who have started planting strings — biological-warfare references, or politically sensitive material aimed at Chinese models — that cause offensive agents to shut down on contact. Deception flips that from an attacker trick into a defensive primitive, and the way an agent reacts can even fingerprint which model it was.

3. It is already happening: a canary caught Claude Code

Smith argues the AI risk customers actually experience day to day is not (yet) the external offensive agent but the sanctioned internal one overstepping its bounds.

"A couple of days into a deployment with a customer, a production Kubernetes pod lit up, and someone had pulled a canary token out of that pod and started trying to use it, and it, it was located to an engineer's laptop. Guess what? It was Claude Code. And the, the, the team didn't even know there was a path from that engineer's laptop all the way to that production Kubernetes pod." — Andy Smith

The point that lands for cloud architects is the unknown path. A coding agent found a route from a developer laptop into a production pod that the team did not know existed and the canary caught it without anyone having written an alert for that specific scenario.

4. You do not need to be "mature" to start and AI removes the old blockers

The long-standing objection to deception is that it is an advanced-maturity move, and that the SIEM comes first because compliance requires it. Smith and Sima both push against that.

"The reality is, like no one ever got fired for building a SIEM... And you come back six months later, they're still building the SIEM... our proposal has been to them, like, 'Hey, why don't we just get some deception?... we could actually cover four or five of your most critical systems with deception, implement assume breach, get these high fidelity detections. Like, we could do that in a week, we could do that in two weeks.'" — Andy Smith

Smith's other argument is that LLMs remove the two historical blockers — knowing where to place decoys and the effort of deploying them. An LLM can take a real inventory of S3 bucket names and suggest which decoy names would look most attractive to a threat actor, and serverless cloud resources make the decoys nearly free to deploy through existing Terraform and Helm pipelines. Sima's counsel on placement is to target the crown jewels rather than scatter decoys everywhere: "the one advantage we have as defenders is we know where the attackers will want to go."

5. A deliberate caution: deception is additive, not a substitute

Caleb Sima makes a point worth keeping precisely because it resists the vendor pitch: a decoy does not reduce the need to protect and monitor the real asset.

"Just because I have a fake one does not mean that the attacker will hit that fake one... My primary S3 bucket needs to be well-protected... And then maybe on the side I have this other S3 bucket that looks juicy, that hopefully the attacker may or may not hit... I know I need to do number one first, and then I can add number two just in case." — Caleb Sima

Smith agrees, and adds the case that resonates with experienced CISOs: primary controls eventually fail, sometimes by accident — his example was an org that pushed a wrong change to Jamf and disabled CrowdStrike for a day — and deception is what produces a signal in that window. Treat canaries as a layer over well-protected assets, not a reason to protect them less.

Practical takeaways

Identify your crown jewels e.g the specific databases, buckets, and secrets an attacker would target and seed decoy credentials or canary tokens around them.
Use an LLM against your real resource inventory to generate believable decoy names, and deploy serverless decoys through the Terraform and Helm pipelines you already run.
Consider seeding a decoy secret with guardrail-triggering content in your highest-value environments; per Tracebit's lab, it can halt an offensive agent and fingerprint the model.
Keep deception additive: protect and monitor the real asset first, then add decoys as the assume-breach layer that fires when primary controls fail.

Tracebit — Andy Smith's company; deception technology across workstations, cloud, identity, and SaaS
Tracebit agentic-AI deception research — the 93%-to-0% guardrail research referenced in the episode [VERIFY URL resolves before publishing]
Hugging Face security incident disclosure — first-party account of the autonomous-agent breach
Sysdig: JadePuffer / ENCFORGE research — ransomware targeting AI model files
CISA Known Exploited Vulnerabilities Catalog — authoritative list covering the ServiceNow, SharePoint, and Langflow flaws above

Podcast Episode

AI Security Podcast : Episode with Andy Smith

Question for you? (Reply to this email)

🤔 If an AI agent were loose in your environment tonight, what's the one decoy that would catch it before it reached your crown jewels?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

📬 Want weekly expert takes on AI & Cloud Security? [Subscribe here]”

We would love to hear from you📢 for a feature or topic request or if you would like to sponsor an edition of Cloud Security Newsletter.

Thank you for continuing to subscribe and Welcome to the new members in tis newsletter community💙

Peace!

This issue is sponsored by ReTool

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🚨 ADFS Zero-Day Exploited as Microsoft Ships a Record Patch Tuesday: The 40% "Dark Matter" in Every Asset Inventory

Ashish Rajan — Fri, 17 Jul 2026 22:39:34 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Asset Intelligence in the AI Era: You Can't Defend the 40% You Can't See (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

Every major story this week lands on a system that decides who gets in: the ADFS federation service minting trust for Microsoft 365, the SonicWall SSL-VPN edge, the routers CISA and NSA say Russian operators are camped on, the IDE that executes repository contents on open, and a global consultancy's Azure DevOps environment holding other organizations' keys. The institutional response compressed to match — a record Patch Tuesday, a three-day federal remediation deadline, and the first joint EU-UK cyber sanctions package.

Acting on any of it presumes an answer to a question most security programs can't give: do you actually know what you have? This week's episode of AI Security Podcast puts that question to Joe Diamond, CEO of Axonius, in conversation with hosts Ashish Rajan and Caleb Sima. Diamond's numbers from hundreds of CISO conversations: 50-60% confidence in coverage, and roughly 40% "dark matter" they know they can't see. The conversation runs from what counts as an asset in an AI world (including ephemeral agents), to why CMDBs miss the surface that matters, to a three-question framework for starting over.[Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 SonicWall SMA1000 zero-days chained in the wild (CVE-2026-15409, CVSS 10.0 SSRF + CVE-2026-15410 code injection). CISA gave federal agencies until July 17 — patch to fixed firmware on the same clock regardless of sector.
🚨 ADFS zero-day CVE-2026-56155 reaches token-signing keys and is in the KEV catalog. Apply the July 14 update plus KB5121391 DKM ACL hardening, then audit who can read the DKM container.
Russia response week: first joint EU-UK cyber sanctions over the Poland grid attack, plus a CISA/NSA/FBI advisory on Russian router targeting. Ingest the new sanctions list; inventory the edge devices between your sites and your cloud on-ramps.
Cursor executes a repository's bundled git.exe on open — no click, no patch seven months after report. Open untrusted repos only in disposable environments until a fix ships.
Accenture confirmed a breach after an attacker listed 35GB claimed to include Azure PATs and storage keys. Inventory integrator-held credentials and rotate anything you can't confirm out of scope.

📰 THIS WEEK'S TOP 5 SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. Microsoft's record July Patch Tuesday fixes an actively exploited ADFS zero-day that reaches token-signing keys

Primary source: Microsoft KB5121391
Reporting: CISA KEV · SecurityWeek · BleepingComputer
Analysis: CrowdStrike

What Happened

Microsoft's July 14 Patch Tuesday fixed a record number of vulnerabilities — reported as 570 by BleepingComputer and 622 by SecurityWeek, including two exploited zero-days. The sharpest is CVE-2026-56155, an access-control flaw in Active Directory Federation Services: ACLs on the Distributed Key Manager container protecting ADFS token-signing and token-encryption keys require manual hardening, and a low-privileged authenticated attacker can reach those keys and escalate. Microsoft credited its own incident response unit, and CISA added the flaw to the KEV catalog the same day. The other exploited zero-day is a SharePoint Server missing-authentication flaw (CVE-2026-56164). Reporting links the rising patch volume to AI-assisted vulnerability discovery.

Why It Matters

An attacker who reads the DKM keys doesn't defeat authentication user by user — they forge the proof that authentication already happened, and every cloud application federated through ADFS accepts the forged assertion, MFA included. This is the second consecutive week ADFS key material is the story: last week Mandiant showed signing keys recoverable from machine DPAPI, and this week's KEV entry confirms the same exposure class being exploited in the wild. A flaw surfaced by an incident response team is a flaw that already had victims before it had a patch.

Action for defenders: Apply the July 14 ADFS update and the KB5121391 DKM ACL hardening now, ahead of any enforcement date; then audit who can read the DKM container and treat unexpected read access as an incident signal, not a misconfiguration ticket.

2. Two SonicWall SMA1000 zero-days exploited in tandem; CISA sets a three-day federal deadline

Primary source: Rapid7
Reporting: SonicWall notice · CISA KEV · Help Net Security

What Happened

Rapid7's MDR team discovered two SonicWall SMA1000 secure-access appliance flaws exploited as zero-days: CVE-2026-15409, a CVSS 10.0 unauthenticated server-side request forgery in the Work Place interface, and CVE-2026-15410, a CVSS 7.2 post-authentication code injection in the management console. SonicWall confirmed incidents chaining the two SSRF for reach, injection for OS command execution on SMA6210, SMA7210, and SMA8200v appliances. CISA added both to the KEV catalog on July 14 with a July 17 federal remediation deadline under BOD 26-04: patch in three days or disconnect.

Why It Matters

The remote-access appliance is a Tier-0 identity system, and a pre-auth SSRF on its user-facing interface converts into admin-level command execution when chained. The three-day deadline is the operational signal. CISA reserves that compression for exploitation it assesses as ongoing and consequential, and it makes the patch window shorter than most enterprise change-approval cycles. Programs routing SSL-VPN appliance patching through monthly cadence are structurally behind this class of attack.

Action for defenders: Patch all SMA1000-series appliances to fixed firmware now and match the July 17 deadline regardless of sector; review appliance logs for unexpected outbound requests from the Work Place interface and management-console commands that don't map to a named administrator.

🛠 If you only do one thing this week: Run this week's news as an inventory drill. Pick the two act-now items every SMA1000 appliance, every ADFS server and its DKM container readers and time how long it takes to produce a complete list with owners. If the answer is more than an hour, the gap you just measured is Joe Diamond's "dark matter," and closing it is worth more than any single patch you'll ship this month.

☁️ 3. EU and UK issue first joint cyber sanctions after attributing the Poland grid attack to Russia's FSB

Primary source: UK government
Reporting: The Record · BleepingComputer

What Happened
On July 13 the EU and UK announced their first coordinated cyber sanctions package, formally attributing the December 2025 attack on Poland's power grid to the FSB's Centre 16 division. UK officials said the attack failed but could have cut electricity to roughly 500,000 people in winter. The EU sanctioned nine individuals and four entities; the UK added 24 names. The package also attributes a years-long campaign against government ministries, companies, and service operators across at least nine EU member states.

Why It Matters
The precedent is procedural, not symbolic: two jurisdictions that previously sanctioned on separate tracks demonstrated they can attribute and penalize as a bloc, and sanctioned-entity screening now has to track a joint EU-UK cyber list that didn't exist last month. For operators of European critical infrastructure, formal state attribution of a grid attack hardens the regulatory read of NIS2 and national equivalents "state actor targeted our sector" is now a documented fact supervisors will cite.

Action for defenders
If you operate in or sell into the EU or UK, confirm your sanctions-screening and third-party risk processes ingest the new joint designations, and brief your board using the Poland attribution as the concrete scenario behind your NIS2/DORA resilience obligations.

🏥 4. CISA, NSA, FBI and partners warn of Russian state targeting of routers across critical infrastructure

Primary source: CISA
Reporting: NSA advisories

What Happened

On July 14, CISA, NSA, FBI, DC3, and international partners published a joint advisory, "Improve Router Hygiene to Protect Against Russian State-Sponsored Targeting." Russian actors are targeting vulnerable networking devices across critical infrastructure sectors globally — primarily through poorly configured routers, secondarily through known CVEs. NSA released companion guidance on reducing SNMP abuse. The advisory landed one day after the EU-UK sanctions package.

Why It Matters

The device class named here is the one hybrid architectures forgot: routers and edge devices between on-prem environments and cloud on-ramps, which rarely appear in CSPM scope and often run configurations nobody has reviewed since installation. The emphasis on configuration over CVEs is the notable shift the primary vector is SNMP left open and reachable management interfaces, which means vulnerability scanning will report these devices as healthy while they're used for persistent access. Sanctions plus same-week hardening guidance is the pattern governments use when they assess a campaign as active, not historical.

Action for defenders
Inventory every router and network edge device between your sites and your cloud interconnects, verify SNMP exposure and management-plane access against the advisory's checklist, and confirm those devices sit inside someone's monitoring scope rather than between teams.

🛡️ 5. Cursor IDE runs a repository's bundled git.exe on open — no prompt, no patch seven months after report

Primary source: The Hacker News (Mindgard research)
Reporting: Dark Reading

What Happened

Mindgard published details of an unpatched flaw in the Cursor AI code editor on Windows: when a project loads, Cursor's Git path resolution checks the workspace itself, so a file named git.exe committed to a repository root executes automatically when a developer opens the folder. The proof of concept committed a renamed Windows Calculator and it launched on open. The binary runs as the logged-in user with access to source, SSH keys, and cloud tokens. Mindgard reported the issue to Cursor on December 15, 2025 and disclosed publicly seven months

Why It Matters

Every recent developer-workstation compromise in this year's supply chain wave required at least an install or an agent in the loop; this one requires opening a folder. The trust inversion is that the IDE treats repository contents as configuration for itself, so cloning untrusted code is now execution of untrusted code, before any human or AI reads a line. The blast radius is whatever tokens the developer's environment holds. The seven-month unpatched window with no advisory is itself a vendor-risk data point for organizations standardizing on AI-first IDEs.

Action for defenders:
Block or flag repositories containing executables named git.exe (or any binary shadowing tooling names) at your source-control and code-review layer, and require untrusted repositories to be opened only in disposable environments a VM or Windows Sandbox until Cursor ships a fix.

6. Accenture confirms breach after attacker lists 35GB of source code, keys, and Azure tokens

Primary source: BleepingComputer
Reporting: Help Net Security

What happened: Accenture confirmed a security incident after a threat actor using the handle "888" listed 35GB of data for sale, claiming source code, SSH and RSA private keys, Azure Personal Access Tokens, Azure Storage access keys, and internal configuration files. Screenshot evidence showed an Azure DevOps repository being cloned under an accenture.com hostname. Accenture described the incident as isolated, said it had remediated the source, and reported no impact to operations. The access method has not been disclosed, and the scope claims remain the attacker's

Why it matters: Accenture's DevOps environment is not one company's attack surface — it's a concentration point for the code, keys, and infrastructure configuration of the enterprises it builds for. If Azure PATs and storage keys were taken, the exposure question lands on Accenture's clients: which of your pipelines, tenants, or storage accounts could a credential from your integrator's repo reach? The incident converts "our consultancy's security posture" from a procurement questionnaire line into a live key-rotation decision.

Action for defenders: If Accenture (or any external integrator) has built or operated cloud infrastructure for you, inventory the credentials, service principals, and PATs their engagements could have held, and rotate anything that cannot be positively confirmed as out of scope rather than waiting for notification.

🎯 Cloud Security Topic of the Week:

Asset Intelligence in the AI Era: You Can't Defend the 40% You Can't See

Every defender action in this week's news is an inventory test in disguise. The router advisory asks whether you can list the edge devices between your sites and your cloud. The three-day SonicWall deadline assumes you can find every SMA1000 appliance before Thursday. The DKM audit assumes you know who can read a specific container in a specific federation deployment. The Accenture rotation exercise asks which credentials your integrator's repos could reach. Joe Diamond's answer, from hundreds of CISO conversations, is that most programs would fail those tests on roughly 40% of their estate and AI agents are about to widen the gap by adding an asset class most inventories don't model at all. [Listen to the full episode →]

Featured Experts This Week 🎤

Joe Diamond - CEO of Axonius
Caleb Sima — Co-host, AI Security Podcast | White Rabbit
Ashish Rajan - CISO | Co-Host, AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Asset (the five-layer view): Caleb Sima's working model — assets are objects across a stack: network (anything with an IP, including IoT/OT), system (infrastructure, cloud, system services), OS (applications, libraries), app (SaaS applications and their APIs), and data (PII, sensitive data), each to be identified, tracked, and managed.
Asset intelligence: Moving from inventory to action — in Diamond's framing, "now that I have this inventory, what is it that I actually do with it."
Dark matter: Diamond's term (borrowed from CISO conversations) for the ~40% of an environment an organization doesn't actually know about.
CMDB: Configuration management database. Works off network sensors and scanning with partial visibility; leans toward classic IT asset management and may include business processes and physical items.
AI for security vs. security for AI: Diamond's split between AI baked into security platforms (natural-language querying, recommendations) and securing your own AI — cataloging agents and their access across asset classes.
DKM (Distributed Key Manager): The container protecting ADFS token-signing and token-encryption keys, at the center of CVE-2026-56155.
KEV catalog: CISA's Known Exploited Vulnerabilities list; addition starts a federal remediation clock (three days this week for the SonicWall pair, under BOD 26-04)

This week's issue is sponsored by ReTool

💡Our Insights from this Practitioner 🔍

1. The asset problem was never solved and 40% of the estate is dark matter

The industry treats asset management as a solved basic. Practitioner reality, per Diamond, is different: "So in my experience, you know, in most of my conversations recently, which is like numbers in the hundreds frankly, you ask your average CISO, like, is this a problem that they have solved? The answer is a resounding no basically every single time. And what I typically run into is that there's some degree of confidence about like 50 to 60% of like their coverage and the understanding of what their environment looks like, but they kind of recognize that there's, you know, some very high percentage, roughly 40%, of what they think of as, like, dark matter and what they, what they, what they don't actually know about."

The old model of agents on devices, network sensors etc never covered assets that don't take an agent and don't traverse networks you control. Cloud, mobile, and now AI widened a gap that was never closed. Diamond's diagnosis is that value comes from "the blocking and the tackling of the basics" rather than new tooling layered on unsolved fundamentals. This week's router advisory is the live example: the primary vector is configuration nobody has reviewed, on devices nobody owns in the org chart.

2. Define assets as objects in a stack, not entries in a spreadsheet

Caleb Sima's definition reframes the problem: "I would love to just make sure we tell the readers that in my opinion, asset identification and understanding your landscape is the most critical thing you could possibly do. And so most people don't understand that. They think that this is, a sort of nice to have, but in reality this is one of the first things."

His model runs five layers: network (anything with an IP, IoT and OT included), system (infrastructure, cloud, system services), OS (applications and libraries), app (SaaS and APIs), and data (PII, sensitive data). Each layer holds objects that should be identified, tracked, and managed. The practical use: this week's stories each live at a different layer — routers at network, SMA appliances at system, Cursor at OS, the integrator's Azure DevOps at app, the DKM keys at data. A program confident at one layer can be blind at another.

3. Asset intelligence is not your CMDB — pair them, don't confuse them

CMDBs work off network sensors and scanning, which is partial by construction, and they lean toward classic IT asset management. Diamond's customer example — BlueLinx uses the two together: "They will take the delineation or the delta that they have between CMDB and what it is they, they see with Axonius and flush it from Axonius back into their CMDB. So they, that way they make up that 40% surface area that CMDB was missing."

The harder problem, Diamond argues, is intersection: what an IoT device talks to, which identities and service accounts it uses. That requires a data pipeline doing correlation, enrichment, and deduplication across IT systems and security controls the difference between knowing you have 20,000 assets and knowing which of them can reach the credential your integrator just lost.

4. AI agents are an asset class ephemerality doesn't exempt them

The definitional debate (is an agent the running loop, the app, or a role expressed as a prompt?) is unresolved, and Diamond concedes "all of us are learning this together." His operating answer borrows the container precedent:

"Would you wanna track those containers as assets just because they're up for a short period of time? Like, those are still a part of your attack surface even if they're up for only, say, 24 to 36 hours."

The same applies to an agent that ran once: "you still wanna catalog it, you still wanna know what it did, you still wanna have some sort of root cause analysis if something does go wrong with that agent."

Dead agents stay in the catalog marked not-live. Governance scope extends to token utilization, identities and service accounts leveraged, and authorization scope the same intersection questions as any other asset class, including agents embedded in third-party platforms like Salesforce and Notion.

5. AI can build, but it can't operate context is the missing half

Sima's thesis, from the closing discussion of why AI hasn't dissolved the vendor moat: "the moat actually turns out to be pretty obvious. It's existed forever, which is accountability, the consistency, ability to scale, benefits from other customers. With AI you can build, but at least today you can't operate and manage"

What AI lacks for operations is context about large infrastructure systems. A continuously correlated asset graph is exactly that context, and Diamond positions it accordingly:

"I think that the context that you can get from Axonius, where you, you can't really get that data and that context in any other way without a lot of manual curation, is the, is, like, really the ground truth for AI."

Sima's build-vs-operate test is worth applying to your own automation roadmap: an agent can write the remediation, but deciding what to remediate first requires knowing how everything is laid out, its history, its configuration, and how it's changing.

6. The traditional UI goes away — everything becomes a prompt

Diamond's strongest forward call, on a three-to-five-year horizon: "It's like I actually do see in the next three to five years the traditional UI going away and everything basically becoming a prompt, effectively."

He ranks the shift above the cloud and mobile transitions: "I actually look at this from an AI perspective as frankly the biggest change that we've seen in, in the industry in our lifetimes. Like, this is not the movement of on-prem to cloud. Like, this is not, the proliferation of mobile. This is like the creation of the internet."

The near-term consequence he names is the death of the static dashboard in favor of fluid questions of your data, including inverse queries ("what am I not thinking about that I should be thinking about?"). Rajan's variant is an API-first read: the interface collapses into calls a SOC analyst's tooling makes mid-incident — who owns this asset, what else is linked to it. Diamond's reconciliation: prompt, API, and dynamically generated dashboard converge, because "what's behind the, the chat prompt is a bunch of APIs, right?"

7. Start with three questions — and the CrowdStrike outage is the proof case

Diamond's framework for approaching asset intelligence in an AI world: "it's three simple questions. What do you actually have? So every device, identity, SaaS app, cloud workload, OT asset, AI agents, AI permissioned accounts. How do you know what you have across all those different dimensions continuously, and not just as like some CMDB snapshot, which is only gonna give you partial coverage anyway?"

Questions two and three: "what are the exposures that we have across that estate that actually matter? So this isn't just like a CVE list. It's the combination of, you know, the asset plus the identity, plus the integration, plus the data sensitivity that together is what actually formulates the risk. ... now that you have that information How do you act on it today in minutes? And how do you do that without leaving whatever platform it is that you have a choice? So it's like that gets you to a place of real governed action with an actual audit trail, not like, uh, puts you in a scenario of opening a ticket and hoping for the best."

The proof case is the CrowdStrike faulty-update outage: customers with a live asset graph identified every device running the bricked version and remediated in days, while others were down longer. The same mechanism answers this week's tests finding every SMA1000 appliance by Thursday, every router outside monitoring scope, every credential an integrator could have held. If you can't answer question one, Diamond's advice is to start there and not skip ahead.

Microsoft KB5121391 — ADFS DKM container ACL hardening — the manual hardening steps behind CVE-2026-56155
CISA KEV alert, July 14 — the ADFS and SonicWall additions and deadlines
Rapid7 — SonicWall SMA1000 zero-day disclosure — technical detail on the exploited chain
CISA/NSA joint advisory — router hygiene against Russian state targeting — the inventory checklist for edge devices
UK government — joint EU-UK sanctions announcement — the new designations for screening processes
Axonius — this week's guest's company (asset intelligence platform; episode sponsor)
AI Security Podcast — full episode audio and more conversations like this one

Podcast Episode

AI Security Podcast : Episode with Joe Diamond

Question for you? (Reply to this email)

🤔 How much of your environment is "dark matter" — and would your CISO give the same number you would?

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🚨 FortiBleed Turns 430,000 Firewalls Into a Ransomware Feed: Why "Exploitable" Beats "Reachable"

Ashish Rajan — Thu, 09 Jul 2026 22:22:44 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Reachable vs. Exploitable — The Distinction That Decides What You Actually Fix (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

The week did not hand us a single marquee breach. It handed us five variations on the same failure: the credential or key an attacker wants is now reachable, and the only question left is whether the surrounding context makes it exploitable. FortiBleed turned tens of thousands of exposed FortiGate firewalls into a working credential feed for two ransomware crews. A Langflow authorization-bypass flaw let one operator read other tenants' flows and walk off with their LLM and AWS keys. Fake Paysafe and Skrill SDKs sat in build pipelines returning fake success while reading AWS and GitHub tokens out of the environment.

That mechanic is exactly what Harry Wetherald, co-founder and CEO of Maze, spends his days on. His argument: reachability tells you a vulnerability might be exploitable; exploitability tells you an attacker actually has everything needed to trigger it — and reasoning across code and cloud context to answer the second question is the shift AI finally makes possible.[Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 FortiBleed is now a ransomware pipeline. SOCRadar tied the mass FortiGate credential theft (~430,000 firewalls targeted) to the INC and Lynx crews. Reset FortiGate admin and VPN credentials and enforce MFA today — the exposure already happened at config-read time.
🚨 A CVSS 6.1 flaw did the real damage. CISA's July 7 KEV batch includes Langflow's cross-tenant IDOR (CVE-2026-55255), used to steal other tenants' LLM and AWS keys, alongside a CVSS 10.0 ColdFusion flaw exploited within hours. Patch by July 10; pull exposed Langflow behind auth and rotate every reachable key.
Fake payment SDKs raided build pipelines. 17 typosquatted Paysafe/Skrill/Neteller packages on npm and PyPI harvested AWS, GitHub, and npm tokens from CI runners. Audit runner env-var scopes; move build secrets to short-lived OIDC.
Your own bucket can be turned against you. Unit 42 detailed a global-namespace bucket-hijacking flaw that reroutes live data streams to an attacker-owned bucket with a reused name. Never delete-and-forget a bucket name referenced anywhere.
ADFS can hand over a live signing key. Mandiant showed active ADFS token-signing keys recoverable from machine DPAPI, enabling MFA-bypassing SAML forgery. Audit AutoCertificateRollover state and certificate drift.

📰 THIS WEEK'S TOP 5 SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. FortiBleed credential-theft campaign confirmed as a ransomware pipeline for INC and Lynx

Primary source: BleepingComputer
Reporting: SOCRadar STRU · Recorded Future · SecurityWeek

What Happened

SOCRadar's threat research unit tied the mass FortiGate credential-theft campaign known as FortiBleed to the INC and Lynx ransomware operations. This is the first confirmed link between the harvesting and downstream ransomware deployment. Investigators found an operator with FortiBleed infrastructure access logged into both the INC and Lynx negotiation panels, and INC victims overlapping with FortiBleed data. The campaign is assessed to have targeted more than 430,000 internet-facing FortiGate firewalls, deployed packet sniffers on roughly 19,000 devices, and cracked configuration-file hashes into verified working administrator credentials for tens of thousands of systems.

Why It Matters

The FortiGate SSL-VPN is the identity edge for most hybrid enterprises, and a cracked admin credential there is not a vulnerability to patch because it is a valid login that survives patching. The credential inventory is now demonstrably feeding two active ransomware crews, turning "we have MFA-less VPN admins" from a hygiene finding into a named, in-progress ransomware precursor.

Action for defenders: Reset all administrative and VPN credentials on internet-facing FortiGate devices, enforce MFA on every admin and remote-access account, and pull VPN auth logs for anomalous admin logins and any sign of packet-capture tooling.

2. CISA adds four exploited flaws to KEV, including a Langflow cross-tenant IDOR that steals LLM and AWS keys

Primary source: CISA KEV alert
Reporting: The Hacker News
Analysis: Sysdig

What Happened

On July 7 CISA added four actively exploited flaws to the KEV catalog with a July 10 federal deadline: Adobe ColdFusion path traversal CVE-2026-48282 (CVSS 10.0), two JoomShaper/PageBuilder unauthenticated file-upload RCEs (CVE-2026-56290 and CVE-2026-48908, both CVSS 10.0), and Langflow authorization-bypass IDOR CVE-2026-55255 (CVSS 6.1). Sysdig reported a single operator chaining the Langflow IDOR with an unauthenticated RCE (CVE-2026-33017) against internet-exposed instances between June 22 and June 25, using the cross-tenant IDOR to read other tenants' flows and steal their LLM-provider and AWS keys. The ColdFusion flaw was exploited within hours of disclosure.

Why It Matters

The CVSS ordering is inverted from the risk — the 6.1 Langflow bug, not the 10.0s, is the one that handed an attacker a live credential set for someone else's cloud account. An AI-orchestration platform is a credential vault, and a cross-tenant IDOR against it is a supply route into every cloud those flows touch.

Action for defenders: Patch all four by July 10; for Langflow, pull any internet-exposed instance behind authentication immediately and rotate every LLM-provider and cloud key any hosted flow could reach — treat exposed instances as already harvested.

🛠 If you only do one thing this week: Find every internet-exposed AI-orchestration or agent platform (Langflow and anything like it), put it behind authentication, and rotate every LLM and cloud key it could reach. This week's Langflow story is the proof that a "medium" CVSS flaw on a credential-holding platform is the one that actually converts to cloud compromise — reachable became exploitable the moment those keys were sitting there.

☁️ 3. Fake Paysafe, Skrill and Neteller SDKs on npm and PyPI harvest CI/CD secrets

Primary source: Socket
Reporting: BleepingComputer · The Hacker News

What Happened
Socket flagged a coordinated cluster of 17 malicious packages — 13 on npm, 4 on PyPI, typosquatting the Paysafe, Skrill and Neteller payment SDKs, published July 7. The packages expose the expected payment APIs and return fake success responses while collecting environment variables and exfiltrating them, including via an Ngrok endpoint and an AWS-hosted C2. Captured variables include PAYSAFE_API_KEY, AWS_SECRET_ACCESS_KEY, GITHUB_TOKEN and NPM_TOKEN. The npm packages were flagged as malicious within roughly six minutes of publication, each shipping four rapid versions with per-version obfuscation keys to defeat hash-based tracking.

Why It Matters
The target is not the payment platform, it is the CI runner, a package that pretends to process payments while reading AWS and GitHub tokens out of the build environment turns a routine install into cloud-credential exfiltration. The six-minute flag time helps teams using package firewalls and does nothing for teams that pull latest on every build, because the payload runs at install.

Action for defenders
Block the named packages, audit CI/CD environment-variable scopes so a compromised install cannot read cloud or registry tokens, and move build-time secrets to short-lived OIDC tokens rather than static keys in the runner environment.

🏥 4. Unit 42 details a cloud bucket-hijacking flaw that reroutes your data to an attacker-owned bucket

Primary source: Palo Alto Networks Unit 42
Reporting: The Hacker News

What Happened

Unit 42 described a bucket-hijacking technique affecting major cloud providers, which it characterizes as a fundamental architectural issue rather than a single-vendor bug. Because storage-bucket names occupy a globally unique namespace, an attacker who learns the name of a decommissioned or deletable bucket can delete it and immediately recreate it under their own account with the same name, that was silently rerouting any data stream still writing to that name (critical logs, telemetry, sensitive data) into attacker-controlled storage. Unit 42 notes the echo of Aqua Security's 2024 "Bucket Monopoly" method and says there is no evidence of in-the-wild abuse to date.

Why It Matters

The trust assumption being broken is that a bucket you configured as a destination stays yours. Global-namespace reuse means a name you stop owning can become someone else's inbound pipe without a single credential being stolen, which reframes bucket naming and lifecycle from housekeeping into a data-exfiltration control.

Action for defenders
Inventory every hard-coded bucket destination in logging, backup, and data pipelines; never delete-and-forget a bucket whose name is referenced anywhere, and adopt naming with account-scoped random suffixes so a released name cannot be re-registered as a data sink.

🛡️ 5. Mandiant recovers active ADFS token-signing keys from machine DPAPI, enabling MFA-bypassing SAML forgery

Primary source: Mandiant / Google Cloud Threat Intelligence
Reporting: SecurityBrief

What Happened

Mandiant disclosed that in ADFS deployments where AutoCertificateRollover is disabled and certificates are rotated manually, configuration drift can leave an active token-signing key exposed in machine-scoped DPAPI while the Windows Internal Database still references a stale certificate. An attacker who recovers that live private key can forge SAML assertions for any user in the federated environment, reaching ADFS-tied applications including Microsoft 365 and Entra ID while bypassing MFA. The technique avoids touching LSASS and the live ADFS process, reducing the telemetry most monitoring relies on. Mandiant frames it as an evolution of the Golden SAML attack.

Why It Matters

This turns a documentation-level operational detail — "we rotate ADFS certs by hand" into a full cloud-identity compromise, because the forged SAML token is trusted by every downstream cloud app federating through ADFS. The MFA bypass is the sharp edge: the control most enterprises treat as their identity backstop is not in the path when the assertion itself is signed with a valid key.

Action for defenders:
Audit ADFS for AutoCertificateRollover state and any drift between the certificate the service is actively signing with and the record in the configuration database; where manual rotation is used, confirm no orphaned active signing key remains recoverable from machine DPAPI.

6. European Commission presents an Action Plan on Cybersecurity and Artificial Intelligence

Primary source: European Commission
Reporting: Commission press corner · MLex

What happened: On July 7 the European Commission presented its Action Plan on Cybersecurity and Artificial Intelligence, framed around defending against AI-accelerated attacks and enabling safe use of AI in security. It directs the Commission to strengthen Europe's capacity to evaluate AI models before they enter the EU market in line with the AI Act, to work with ENISA on a European Blueprint for secure access to advanced AI systems for cybersecurity, and to stand up a secure platform to test AI for cybersecurity by the end of 2026. Analysts note the plan emphasizes implementing existing frameworks rather than new legislation.

Why it matters: Pre-market model evaluation tied to the AI Act moves AI security from a vendor-attestation question to a market-access condition, which changes procurement for any cloud or security platform shipping model capabilities into the EU. Expect model-provenance and secure-access-to-AI questions to surface in supervisory conversations the way DORA incident-reporting expectations did this year.

Action for defenders: If you operate in or sell into the EU, map which AI capabilities in your cloud stack will fall under AI Act pre-market evaluation, and track the ENISA secure-access Blueprint as an input to AI governance and vendor assessment.

🎯 Cloud Security Topic of the Week:

Reachable vs. Exploitable — The Distinction That Decides What You Actually Fix

Two of this week's stories are token problems wearing different clothes. Azure CLI issued tokens through an OAuth flow that never met the policy; SimpleHelp accepted tokens it never verified were signed. Kahn's episode is the conceptual layer under both: as autonomous agents multiply, the same two questions is this identity real, and is this access still appropriate — stop being solved by static permissions and start requiring standards built for identities whose goals change every day. The through-line for the week is that the management and identity plane is where the damage now lands, and agent identity is the version of that problem heading straight for every enterprise that shipped an agent this quarter. [Listen to the full episode →]

Featured Experts This Week 🎤

Harry Wetherald — Co-founder & CEO, Maze
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Reachability: Whether an attacker can at least get to the vulnerability — in cloud, roughly whether it can be reached from the network; in code, whether the vulnerable function is actually active rather than dead code. Signals only that something might be exploitable.
Exploitability: Whether all the context is present for an attacker to actually trigger the specific vulnerability (e.g., can a triggering request pass input sanitization and surrounding controls). The deeper determination AI now automates.
Cross-tenant IDOR: An Insecure Direct Object Reference where an authenticated user references another tenant's object (here, another Langflow tenant's flow ID) to read data — in CVE-2026-55255, other tenants' LLM and AWS keys.
FortiBleed: The name for the mass credential-compromise campaign against internet-facing FortiGate firewalls, extracting configuration files and cracking stored credential hashes, now linked to INC and Lynx ransomware.
Golden SAML: An attack in which a stolen ADFS token-signing key is used to forge SAML assertions for any federated user, bypassing MFA; Mandiant's machine-DPAPI key recovery is an evolution of it.
Bucket hijacking (global-namespace reuse): Re-registering a released, globally unique storage-bucket name under an attacker account to silently receive data still being written to that name.
SCA / SAST: Software Composition Analysis (third-party/dependency code, e.g., CVEs) and Static Application Security Testing (your own code). Maze Code ships one product for each.
"Security brain": Wetherald's term for a persistent, enriched, cached context layer plus threat model and human-defined priorities that a coding agent queries before and while writing code — his proposed successor to "shift left."

This week's issue is sponsored by Maze

💡Our Insights from this Practitioner 🔍

Reachable vs. Exploitable, and the AI-Native AppSec Program

Harry Wetherald has spent two years building AI agents that reason across code and cloud. The through-line: the tooling finally exists to answer the question that actually matters, but only for teams that treat reliability and cost as first-class engineering problems rather than vendor talking points.

1. Reachable means "the code is alive." Exploitable means "the attacker has everything they need."

Wetherald's opening move is to separate two words the industry uses interchangeably. Reachability is a blunt filter; exploitability is the full-context judgment call.

"So that difference really is a difference between reachable, but is the code alive i, in the simplest terms? And exploitable is, is all the context kind of there for the attacker to actually be able to trigger the specific vulnerability?"

In code, that means first filtering out dead code, then testing whether a request that triggers the vulnerability can actually pass input sanitization and the controls around the application. The same logic applies in cloud and it maps directly onto this week's Langflow story, where "reachable" and "exploitable" were only a set of exposed keys apart.

2. Untuned AI is confidently wrong, the failure mode moved from "obvious false positive" to "detailed, plausible false positive"

Old rule-based scanners drowned teams in obvious noise. Out-of-the-box models are more accurate on a first pass but wildly inconsistent run to run, and their errors now arrive wrapped in convincing detail.

"they give very confident but sometimes wrong results 'cause they'll go very deep in an investigation, take one wrong turn along the way, and then give you the wrong answer."

The fix is training agents to run investigations reliably and to catch their own mistakes, plus heavy monitoring and validation on top. Run the same agent on the same data in a poorly built system, Wetherald notes, and it can return a different answer five times out of ten.

3. The frontier labs' security tools test as inferior and more expensive

One of the sharper takes in the conversation. Wetherald argues the lab-built security offerings are side projects that can't match tools honed for a year or two on a specific domain, and — unusually — they cost more, because the labs' incentive is to sell tokens.

"what we've seen from Claude Code Security and from other, the Google and, and OpenAI equivalents is when you test them, they're far inferior to the more domain-specific tools that people have spent a year, two years honing, refining, training, et cetera."

His buying advice: evaluate them the way you once evaluated bundled Microsoft or Google security — fine if "good enough" for the use case, but if security is strategic, go to specialists and avoid locking into one lab.

4. Cost IS accuracy, the case for 100x optimization, not 20%

Because customers have a fixed budget ceiling, every place you cut cost without losing accuracy frees budget to spend on deeper analysis elsewhere. Cost engineering isn't a margin lever; it's the accuracy lever.

"if you run Mythos, uh, on every PR for a 10,000-person software company, it comes out as $52 million a year."

Wetherald's grounding story: the first Maze cloud run at a Fortune 100 customer extrapolated to roughly "$4 million a week" — about the company's total funding at the time — which forced cost to become a core product constraint. The takeaway for buyers is to ask how a vendor cuts cost by ~100x (dynamic routing across expensive and cheap models, or no model at all for some steps), not by a token percentage.

5. The number-one buyer red flag is a black box

Because LLM-based tools inherently produce long chains of reasoning, a modern AI security product that behaves like an opaque box has no excuse. Auditability — "how did you reach that decision" — is the thing to demand.

"If you're talking to an, like, AI-based product and it looks like a black box and it's not telling you in really clear detail what it's doing, I would run a mile."

His second buyer test: ask what the vendor built on top of the out-of-the-box models. If anyone could replicate it by pointing a frontier model at a codebase with a few prompts, the vendor has added nothing.

6. Tear down the AppSec/cloud wall — and put a "security brain" behind the coding agent

Wetherald argues the historic separation of AppSec and cloud security was an artifact of tooling, not of the risk — the same underlying issue is often handled by two teams. LLMs act as a translator across the two domains, so the wall should come down.

"We shouldn't be sat here in five years' time and going, our AppSec and our cloud security program runs completely separately."

Looking forward, he sees coding agents (Cursor, Claude Code, Devin) needing a security "brain" to call — an enriched, cached context layer plus the org's threat model and priorities — rather than each agent crawling raw data in the moment it writes code. That, in his view, is the useful successor to "shift left."

7. Fully delegate the verifiable decisions; keep humans on remediation — for now

Where a decision is logically verifiable, Wetherald wants humans out of the loop, because it has to run across potentially millions of findings.

"where it's kinda logically verifiable, so exploitability ... in both camps, cloud and AppSec ... I don't think we should be having humans in the loop of that decision."

Remediation is where teams still want a human on the final step. The path to more automation is data-driven, not a switch: if an action has succeeded several times, consider automating the next one, starting with simple, well-understood code and cloud fixes.

CISA Known Exploited Vulnerabilities Catalog — authoritative list of what's being exploited; the July 7 additions carry a July 10 deadline
Palo Alto Networks Unit 42 — Cloud Bucket Hijacking Risks — the global-namespace reuse technique in detail
Mandiant — Recovering Active ADFS Signing Keys via Machine DPAPI — the Golden SAML evolution and detection guidance
Socket — npm/PyPI Payment-SDK Typosquat Campaign — package list and IOCs
European Commission — Action Plan on Cybersecurity and AI — the policy backdrop to the week's AI-exposure stories
Maze — Harry Wetherald's company; recently launched Maze Code (SCA + SAST-style AI agents)

Podcast Episode

Cloud Security Podcast : Episode with Harry Wetherald

Question for you? (Reply to this email)

🤔 When you triage a finding, do you stop at "reachable" or do you actually confirm it's exploitable before it costs your team a week?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

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Peace!

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🔑 MFA Was On and Attackers Walked Around It: Securing the Agent Control Plane with Open Source

Ashish Rajan — Wed, 01 Jul 2026 19:34:00 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Agent Identity and the Control Plane — Why "MFA Is On" Stopped Being the Right Question(continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

The week did not produce one dramatic incident. It produced a cluster with a single tell: SimpleHelp remote management, Cisco Unified Communications Manager, Kemp LoadMaster, Oracle PeopleSoft's environment-management hub, and the Azure CLI login path all took direct fire in five days. These are not customer-facing apps. They are the interfaces that administer other systems, and the reader's exposure this week is less about any one CVE than about which admin surfaces are internet-reachable and whether their authentication actually covers every path in.

Underneath that runs a sharper thread. In the Azure CLI spray and the SimpleHelp bypass, multi-factor authentication was present and got skipped anyway, not brute-forced but routed around, through a deprecated OAuth flow in one case and a forged, unsigned identity token in the other. That is the exact failure mode Ely Kahn spends this episode on — the Chief Product Officer at Okta, who joined five months ago specifically to work on agent identity after co-founding Sqrrl (acquired by AWS), launching AWS Security Hub, and serving as CPO at SentinelOne. His argument: the building blocks of identity have not changed, but the environment has, and the place teams keep getting hurt is the long-lived, overprivileged token.[Listen to the episode]

⚡ TL;DR for Busy Readers

Nissan employee data breached via Oracle PeopleSoft zero-day (CVE-2026-35273): the victim-disclosure phase of a management-hub flaw — HR system-of-record for four countries reached through one internet-exposed admin component. Confirm no PSEMHUB instance is externally reachable.
🚨 Azure CLI password spray hit 78 accounts by routing around Conditional Access: 81M attempts against the ROPC OAuth flow that issues tokens without an interactive MFA prompt. Set Conditional Access to All cloud apps / All client app types and block ROPC.
🚨 SimpleHelp RMM OIDC bypass (CVE-2026-48558) added to KEV, July 2 deadline: the tool checked that a token existed but never checked it was signed by the real issuer — a supplier-shaped blast radius across every downstream customer. Patch above 5.5.15 and rotate reachable cloud/AI keys.
Cisco Unified CM SSRF (CVE-2026-20230) actively exploited, federal deadline already passed June 28: blast radius decided by a config flag (WebDialer enabled), not a version number. Query for WebDialer enablement, not just patch level.
Kemp LoadMaster pre-auth RCE (CVE-2026-8037) now has a public exploit: root on a load balancer is a position above the apps it fronts. No confirmed in-the-wild exploitation yet — patch or restrict the management API before scanning catches up.📰 THIS WEEK'S TOP 5 SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. Nissan discloses employee data breach from the Oracle PeopleSoft zero-day (CVE-2026-35273)

Primary source: BleepingComputer
Reporting: SecurityWeek, Infosecurity Magazine

What Happened

Nissan North America notified current and former employees that attackers exploiting Oracle PeopleSoft zero-day CVE-2026-35273 accessed personnel records. The flaw is a CVSS 9.8 unauthenticated SSRF-to-RCE bug in the PeopleTools Environment Management Hub (PSEMHUB); Oracle shipped emergency mitigations on June 10. Nissan filed its breach notification June 25, with public reporting on June 29. Researchers attribute the campaign to a cluster tracked as UNC6240, linked to the ShinyHunters extortion group; exposed data spans employees in the US, Canada, Mexico, and Brazil and may include payroll, banking, and government ID numbers.

Why It Matters

The CVE itself was covered in the June 18 brief; this is the victim-disclosure phase, where the enterprise cost of a management-hub flaw becomes visible. PSEMHUB administers PeopleSoft environments, so one exposed instance handed attackers the HR system of record for four countries. The exploitation window (May 27 to June 9) closed before Oracle's June 10 mitigation, so patch-on-disclosure was already too late for anyone internet-exposed. Programs that inventory only production customer apps and treat internal HR/ERP admin components as low priority keep learning about these through a breach-notification letter.

Action for defenders: Confirm whether any PeopleSoft PSEMHUB instance is reachable from outside your management network; if PeopleTools is at 8.61/8.62, verify the June 10 Oracle mitigation is applied and hunt for SSRF-pattern requests to PSEMHUB back to late May.

2. Azure CLI password-spray campaign compromised 78 accounts by routing around Conditional Access

Primary source: Huntress
Reporting: The Hacker News, SecurityWeek

What Happened

Huntress documented an automated password-spray campaign against Microsoft's Azure CLI login path, running more than 81 million attempts between June 12 and June 26 from an IPv6 range operated by LSHIY LLC (AS32167). At least 78 accounts across roughly 64 organizations were compromised. The attackers replayed breach-corpus username and password pairs against the Resource Owner Password Credentials (ROPC) OAuth flow, which posts credentials straight to the /token endpoint and issues tokens without an interactive MFA prompt.

Why It Matters

This is MFA that was configured and still bypassed. The Conditional Access policies at the affected orgs were real but scoped, enforced for admin portals or named apps rather than "All cloud apps" and "All client app types," and ROPC lives in the gap. The control did not fail as code; it failed as coverage. This is the news-side proof of what Kahn argues in the interview: the question stopped being "is MFA on?" and became "which authentication flows can reach a token without ever hitting the policy?"

Action for defenders: In Entra, set Conditional Access to All cloud apps / All client app types, enable the userStrongAuthClientAuthNRequired tenant setting to block ROPC outright, then pull sign-in logs for ROPC/legacy-auth token grants over the June 12–26 window.

🛠 If you only do one thing this week: Pull your Entra sign-in logs for ROPC and legacy-auth token grants over the June 12–26 window, then flip Conditional Access to All cloud apps / All client app types. Both this week's identity incidents (Azure CLI and SimpleHelp) succeeded by reaching a token through a path the policy never covered — closing the uncovered flow is the 30-minute action that maps directly to the week's editorial thesis.

☁️ 3. SimpleHelp RMM OIDC bypass (CVE-2026-48558) added to KEV, exploited to drop the Djinn stealer

Primary source: CISA KEV
Reporting: Help Net Security
Analysis: Arctic Wolf, Horizon3.ai

What Happened
CISA added CVE-2026-48558 to the KEV catalog on June 29 with a July 2 deadline. The flaw is an OIDC authentication bypass in SimpleHelp RMM (versions 5.5.15 and earlier, plus 6.0 pre-releases): when OIDC is enabled, the software does not verify the cryptographic signature on identity tokens, so a remote unauthenticated attacker can forge a token, land a fully authenticated technician session, and bypass MFA in some configurations. Blackpoint Cyber documented attackers using the forged session to deploy a Node.js loader (TaskWeaver) and a new credential stealer (Djinn).

Why It Matters
RMM is a supplier-shaped blast radius. SimpleHelp is widely used by MSPs and IT-service providers, so one forged technician token is not one endpoint but every downstream customer that provider reaches. The failure is precise: the tool checked that a token existed but never checked it was signed by the real issuer, so MFA sat behind a step the attacker could skip. This is Kahn's identity-proof point made concrete knowing a session is who it claims to be is the whole game, and reporting that the forged access harvested cloud and AI API keys turns a remote-support compromise directly into cloud-account access.

Action for defenders
If you run SimpleHelp, patch above 5.5.15 immediately and rotate any cloud/AI provider keys reachable from serviced endpoints; if a vendor uses SimpleHelp to support you, ask them today which version they run and whether they have hunted for TaskWeaver/Djinn indicators.

🏥 4. Cisco Unified CM SSRF (CVE-2026-20230) added to CISA KEV with a June 28 federal deadline

Primary source: CISA KEV alert (2026-06-25)
Reporting: BleepingComputer, The Hacker News

What Happened

CISA added CVE-2026-20230, a CVSS 8.6 server-side request forgery bug in Cisco Unified Communications Manager, to the KEV catalog on June 25 and set a June 28 remediation deadline for federal agencies under BOD 26-04. Cisco patched the flaw on June 3; the SSRF targets the WebDialer service and lets an unauthenticated attacker write arbitrary files to the underlying OS, which chains to root-level code execution. The security firm Defused reported in-the-wild exploitation beginning the weekend of June 21–22, after a public write-up exposed the file-write path.

Why It Matters

The exploit reached active use only after a proof-of-concept made the SSRF-to-file-write step concrete - a live example of the disclosure-to-exploitation gap collapsing once someone publishes the hard part. Blast radius here is set by a configuration flag, not a version number: only deployments with WebDialer enabled are exploitable, and WebDialer is off by default. That inverts the usual triage. A program sorting purely by CVSS will either over-rotate on patched-but-not-vulnerable hosts or miss the enabled ones.

Action for defenders
Query your Unified CM estate for WebDialer enablement rather than version alone; where it is on and not needed, disable it, and confirm the June 3 Cisco patch is applied on any host exposing the service.

🛡️ 5. Public exploit chain drops for critical Progress Kemp LoadMaster pre-auth RCE (CVE-2026-8037)

Primary source: watchTowr Labs
Reporting: The Hacker News, Cyber Security News

What Happened

On June 29, watchTowr Labs published a full technical write-up and working exploit chain for CVE-2026-8037, a CVSS 9.8 pre-authentication RCE in Progress Kemp LoadMaster. The bug is an uninitialized-memory/string-termination flaw in the escape_quotes() function meant to sanitize input before it reaches a shell command; anyone who can reach the appliance API can run arbitrary commands as root with no credentials. Progress published its advisory on June 4 (the flaw was reported by Syed Ibrahim Ahmed of TrendAI Research) and says it has no reports of exploitation, but a working PoC is now public.

Why It Matters

A load balancer sits in front of application traffic and terminates sessions, so root on the LoadMaster is a position above the apps it fronts, not beside them. The clock started June 29: the vendor advisory sat for three-plus weeks at low urgency, and the watchTowr publication is the event that converts it into a mass-scan target because it hands attackers the exact primitive. This is the pre-exploitation window in real time — patch status, not incident response, is still the lever, but only until scanning catches up. To be precise: there is no confirmed in-the-wild exploitation yet, so treat this as a public-PoC story, not an active-exploitation one.

Action for defenders:
Treat any internet-reachable Kemp LoadMaster management API as urgent: apply the Progress fix from the June 4 advisory, and if patching lags, restrict API access to a management network now rather than waiting for exploitation reports.

6. Aflac Japan discloses breach affecting 4.38 million customers

Primary source: SecurityWeek
Reporting: The Japan Times, Security Affairs

What happened: Aflac Life Insurance Japan, the Japan subsidiary of US insurer Aflac, disclosed on June 30 that attackers reached its customer website and related systems, exposing data on about 4.38 million customers and roughly 40,000 agencies. Intruders accessed systems repeatedly between June 15 and June 25, when a traffic surge revealed the activity. Exposed records include names, addresses, dates of birth, and policy details, plus bank account numbers used for premium payments for about 230,000 customers; credit card and national ID numbers were not affected, and Aflac says its US systems are not involved.

Why it matters: For this reader base the signal is subsidiary-and-parent risk, not the raw record count. A US-headquartered insurer's non-US subsidiary was breached on infrastructure the parent likely does not administer or monitor directly, yet the parent's name carries the disclosure. The month of dwell between first access (June 15) and detection-by-load-surge (June 25) is the tell: detection came from a performance anomaly rather than a security control, which is what happens when a subsidiary sits outside the parent's monitoring perimeter. The banking data for 230,000 customers is the part that turns this from notification into fraud exposure.

Action for defenders: If your org has subsidiaries or acquired entities on separately managed stacks, confirm they feed the same detection and logging pipeline as the parent; a load-driven discovery a month in is evidence the perimeter did not extend to them.

🎯 Cloud Security Topic of the Week:

Agent identity: who the agent is, what it can reach, what it's doing and how do you offboard it?

Two of this week's stories are token problems wearing different clothes. Azure CLI issued tokens through an OAuth flow that never met the policy; SimpleHelp accepted tokens it never verified were signed. Kahn's episode is the conceptual layer under both: as autonomous agents multiply, the same two questions — is this identity real, and is this access still appropriate — stop being solved by static permissions and start requiring standards built for identities whose goals change every day. The through-line for the week is that the management and identity plane is where the damage now lands, and agent identity is the version of that problem heading straight for every enterprise that shipped an agent this quarter. [Listen to the full episode →]

Featured Experts This Week 🎤

Ely Kahn — Chief Product Officer, Okta
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Non-human / AI-agent identity: the identity assigned to a software agent or workload rather than a person. Kahn's point is that the building blocks (least privilege, governance, monitoring) are the same as for humans, but agents get new goals constantly, so static permission models break.
ROPC (Resource Owner Password Credentials): an OAuth flow that posts a username and password straight to the token endpoint and returns a token without an interactive MFA prompt — the flow the Azure CLI campaign abused to sit in the gap left by scoped Conditional Access.
OIDC (OpenID Connect): an identity layer on top of OAuth. The SimpleHelp flaw was an OIDC bypass: the software accepted identity tokens without checking their cryptographic signature, so a forged token passed as authentic.
XAA (Cross-App Access): as Kahn describes it, "an extension of OAuth" and an open standard led by Okta with partners including Anthropic, letting apps and agents "securely pass the baton to one another" without static master keys or per-action consent pop-ups. Now built into the MCP server protocol. Developer resource: xaa.dev.
ID-JAG (identity assertion grant): the mechanism under XAA that puts an identity provider in the middle of app-to-app interactions and grants access without consent pop-ups.
SPIFFE: gives every workload or agent a unique, verifiable digital identity, cryptographically encoded into a short-lived document (typically an X.509 certificate) used to prove identity within an environment.
Intent-based security: a model with zero standing privilege where, at tool-invocation time, an assessment checks whether the tool call aligns with the agent's intent; a dynamic permission is generated for that single call and revoked on completion.
Guardian agents: agents that assess whether a given tool invocation is acceptable and aligned to what the monitored agent was set up to do — the enforcement mechanism when human approval can't scale.
Kill switch / universal logout: an Okta capability, originally for human identities and now extended to agents, to cut off a compromised agent's access immediately and universally.
MCP (Model Context Protocol): the standardized way agents connect to external tools and data; Kahn notes XAA is now part of the MCP server protocol maintained by Anthropic.

This week's issue is sponsored by Maze

💡Our Insights from this Practitioner 🔍

Agent Identity and the Control Plane

Kahn frames agent identity as a paradox: in the mechanics it is not new, and in the environment it is completely new. "In some ways it's not different at all in that you need, uh, least privileged identities. You need to govern those identities through things like IGA. You need to provision access to privileged resources through things like PAM." The building blocks — IGA, PAM, ISPM, ITDR — carry over. What breaks is the assumption underneath them.

1. The breach is the long-lived, overprivileged token

Human roles are stable enough to tolerate static permissions. Agents are not. "You're giving them new problems to solve all of the time, and the most effective agents are the ones that are gonna be very autonomous. And it's very hard to predefine every permission that autonomous agent may need." The workaround teams reach for is the dangerous one:

"People give agents long-lived, broadly scoped permissions to basically do anything that they may need to do in the future, and this is where companies can get into trouble."

Kahn's example is a recent one he keeps unnamed on purpose. "There was a big one in April, a vibe coding company had their entire source code" stolen:

"They had their entire source code stolen basically because of an attacker found a token, a long-lived, overprivileged token associated with the agent that they were using, this was a third-party agent, and then used that to move into the company, move laterally, and ultimately get access to that source code."

That is the same primitive as this week's Azure CLI and SimpleHelp stories: a credential that outlived its need and reached further than it should have.

2. Assume breach on agents, because prompt injection isn't going away

Kahn's reason that identity is "suddenly invited to the cool kids' table" is blunt:

"I think the reason for this is agents will be breached. I don't think this problem of prompt injection is, is gonna go away anytime soon. And we have to assume breach on agents."

Ashish Rajan, who started his own career in identity, agreed from the research side:

"prompt injection is almost an impossible problem to solve with the way we use systems today."

The consequence is a shift in where the effort goes. If you cannot reliably stop the agent from being manipulated, you contain what a manipulated agent can do. In Kahn's words, "ensuring that they have secure, least privilege identities is the highest ROI security action that you can do."

3. XAA is the middle path between master keys and consent-popup fatigue

Ask an agent to check Slack, read a calendar, and update a project board, and historically developers had two bad options:

"Either give that AI agent a permanent master key, like a static API key, which is, as we know, very unsafe if leaked... or bombard the user with constant annoying, 'Do you give permission?' pop-ups every single time the AI tries to do something."

Cross-App Access is Kahn's answer: predefine which apps an agent may reach, then let an identity provider broker access via ID-JAG without per-action prompts. It operates on behalf of a human at the intersection of the human's and the agent's permissions — "so it'll always be least privilege" — or fully autonomously with the agent's own identity. Kahn is emphatic that this is not proprietary lock-in: "this is an open standard. It can be used by anyone," and it is now part of the Anthropic-maintained MCP server protocol.

4. SPIFFE and XAA solve different halves of the same puzzle

Teams that already run SSO and SAML ask which new standard to pick. Kahn's answer is that it is not a choice:

"SPIFFE is, you know, is this, what's the identity of this agent?... versus XAA is really about what resources can that agent connect to."

SPIFFE proves identity inside your environment with a short-lived X.509 credential; XAA governs what that proven agent can reach across apps, which fits SaaS-heavy estates. Kahn also flags newer cross-company work — a Linux Foundation initiative modeled on DNS that would give each agent a "passport ID" that works across domains.

5. Authorization has four maturity levels, and the top one has no standing privilege

Kahn lays out a ladder. Service-account API keys with broad access sit at the bottom ("that's where we need to move away from as quickly as possible because the blast radius is so big"). Coarse-grained scopes with short-lived ephemeral tokens are next — how XAA works. Fine-grained access that honors data sensitivity and labels comes after. The top rung is aspirational:

"the agent has zero standing privilege. No access is given to the agent in terms of standing privileges. And instead, at tool invocation time, an assessment is made as to whether that agent is trying to make a tool call that is aligned with the intent of that agent. And then a dynamic permission is generated and granted for that agent to make just that single tool call, and then is revoked once it's completed."

Because human approval cannot scale to autonomous-agent volume, Kahn expects "guardian agents that are assessing whether that tool invocation is acceptable or not."

6. Nobody is offboarding agents and there needs to be a kill switch

Rajan raised the gap directly: "I don't know anyone who's talking about offboarding an AI agent... People just make agents and they walk away from it." Kahn's answer runs on two tracks. Governance-side, agent offboarding "does look a lot like human off-boarding" — joiner/mover/leaver reviews, access campaigns, and analysis of toxic permission combinations, with AI layered in to flag agents that hold scopes they never use. Runtime-side, there is the kill switch:

"once you've identified that there is malicious or suspicious behavior, you want an ability to cut off agent access immediately and universally. And this is where the idea of a kill switch comes into play."

7. The MVP governance bar is three questions

For a CISO adding agents to an existing identity program, Kahn reduces the bar to three questions: where are my agents (known and shadow, including browser-level and locally installed agents and their MCP servers), what can they connect to (XAA and agentic identity governance), and what are they doing (runtime monitoring through an agent or MCP gateway that logs every tool invocation and feeds anomaly detection). If a team can only start in two places, he is specific:

"knowing where your agents are and importing them into a registry. And then secondly, ensuring that you're at least assigning those AI agents coarse-grained scopes, and not using API access keys with broad permissions."

Which lands back where the week started: the reachable, overprivileged, unverified credential is the exposure, whether it belongs to an admin interface or an agent.

AppSec & DevSecOps Guidance

Podcast Episode

Cloud Security Podcast : Episode with Ely Kahn

Question for you? (Reply to this email)

🤔 Do you know which authentication flows in your tenant can reach a token without ever hitting your MFA policy?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

📬 Want weekly expert takes on AI & Cloud Security? [Subscribe here]”

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🚨 The Klue OAuth Token Breach: Why Stolen Credentials Now Get Used in Seconds, Not Days

Ashish Rajan — Wed, 24 Jun 2026 21:55:58 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Data-first attacks break forensic assumption and logging is the only fix that scales(continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

The pattern across this week's incidents is hard to miss: bug discovery is moving at AI speed while the damage still runs through old, ordinary plumbing — an abandoned OAuth token, a decades-old C bug, stolen RDP credentials. That gap is exactly where Simon Biggs spends his time. Biggs is a cyber incident response specialist on the forensics team at Varonis, with roughly 15 years in the field, starting in UK cyber law enforcement and later doing consultancy IR at NCC Group. Ashish Rajan sat down with him at Infosecurity Europe to ask the question on everyone's mind this year: is there actually a wave of sophisticated AI attacks, and what does forensics look like when the attacker is automating?

His answer is more useful than the hype. AI is not unlocking the impossible — it is lowering the barrier to entry and compressing the timeline. The defensive consequence is concrete and unglamorous: if you can't trace a clear path from your data back to an endpoint, and you haven't classified your data in advance, you won't be able to tell a customer, a regulator, or a contractually-armed third party what was taken.[Listen to the episode]

⚡ TL;DR for Busy Readers

Klue OAuth breach → Salesforce CRM theft: A forgotten, never-revoked OAuth token let attackers exfiltrate CRM data from Klue's customers, with extortion group "Icarus" claiming the theft. Pull your Salesforce Connected Apps OAuth usage and revoke any integration without an owner.
AI is finding old bugs faster than you can patch them: "Squidbleed" (a 1997 Squid proxy flaw) was surfaced with help from Anthropic's Mythos model the same week OpenAI shipped GPT-5.5-Cyber. Treat disclosure-to-exploit windows as effectively zero for perimeter assets.
PixelSmash (CVE-2026-8461) puts RCE in your media-processing tier: Any service that transcodes or thumbnails uploaded media via FFmpeg/libavcodec is exposed pre-auth. Inventory what links libavcodec and patch to 8.1.2.
Attacks are now data-first, not encryption-first: Biggs sees crafted SQL queries pulling credentials and PII within minutes of access. Without database and egress logging, forensics can't tell you what left.
Prepare before the breach: Data classification and a clean audit path from data to endpoint are the difference between "your data is out of scope" and "we don't know." Run a dry-run IR exercise on one critical data store this week.

📰 THIS WEEK'S TOP 5 SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. Klue OAuth breach feeds 'Icarus' extortion across multiple security vendors

Primary source: BleepingComputer
Reporting: TechCrunch, The Hacker News, CSO Online

What Happened

Competitive-intelligence vendor Klue was compromised through a long-lived OAuth credential created years earlier for an abandoned integration and never revoked. The attacker pivoted into Klue's infrastructure, harvested the OAuth tokens Klue used to connect to customers' Salesforce tenants, and ran automated REST API queries to enumerate and exfiltrate CRM records. Salesforce disabled the Klue Battlecards integration on June 11. By June 22, reported victims included Huntress, Recorded Future, Tanium, Jamf, Sprout Social, Gong, and Insurity [VERIFY — victim names from secondary reporting]. A group calling itself Icarus, active since late April, claimed the theft and sent 48-hour extortion emails.

Why It Matters

Initial access was not a zero-day or a phished password — it was a forgotten OAuth token with no expiry. The detection surface is your third-party integration inventory, not your endpoint telemetry. A Salesforce-connected app holds standing API access to CRM data, so one compromised vendor becomes direct CRM exfiltration across its entire customer base without ever touching a customer's perimeter. That several reported victims are themselves security vendors makes the point: blast radius follows the integration graph, not the maturity of the target.

Action for defenders: Pull the full list of authorized connected apps in Salesforce (Setup → Connected Apps OAuth Usage), revoke tokens for any integration without an active business owner, and apply token expiry and IP restrictions to the rest.

2. PixelSmash: FFmpeg decoder flaw (CVE-2026-8461) turns one video file into RCE

Primary source: JFrog Vulnerability Research
Reporting: SecurityWeek

What Happened

JFrog disclosed a heap out-of-bounds write in FFmpeg's MagicYUV decoder, CVE-2026-8461 (CVSS 8.8). A crafted AVI, MKV, or MOV file processed by any application linked against libavcodec can corrupt heap metadata and hijack an internal FFmpeg callback pointer. JFrog demonstrated a full chain overwriting the AVBuffer.free pointer with system() to run arbitrary commands, including RCE against Jellyfin. Confirmed-affected software includes Kodi, mpv, ffmpegthumbnailer (used by GNOME, KDE, XFCE), Jellyfin, Emby, Nextcloud, Immich, PhotoPrism, and OBS Studio. FFmpeg shipped the fix in version 8.1.2 on June 17.

Why It Matters

The vulnerable path runs wherever media gets transcoded or thumbnailed automatically — upload pipelines, NAS appliances, and self-hosted apps like Nextcloud and Immich that decode user-supplied files with no human in the loop. Exploitation is pre-authentication and server-side for any service ingesting media, so the exposure sits in your file-processing tier, not on user desktops. FFmpeg is a transitive dependency most teams don't track, so the question is "which of my running services link libavcodec," not "do we use FFmpeg."

Action for defenders: Inventory services that decode or thumbnail uploaded media, confirm the bundled libavcodec/FFmpeg version, and apply 8.1.2 or your distro's backport. Where immediate patching isn't possible, restrict the decoders and container formats FFmpeg will accept.

🛠 If you only do one thing this week: Run a 30-minute inventory of where stolen credentials or user-supplied files get standing access in your environment — Salesforce Connected Apps and any media-decoding service that links libavcodec. Revoke ownerless OAuth tokens and confirm your FFmpeg builds are at 8.1.2. Both of this week's worst stories (Klue, PixelSmash) start in places most teams never inventory.

☁️ 3. 'Squidbleed' (CVE-2026-47729): a 1997 Squid proxy bug, surfaced by an AI model

Primary source: Calif.io research blog
Reporting: The Hacker News, SecurityWeek

What Happened
Calif.io publicly disclosed a heap over-read in Squid's FTP gateway, CVE-2026-47729, that returns raw heap memory to a requester including other users' Authorization headers, cookies, and API keys. The bug traces to a 1997 commit. Calif credits Anthropic's Claude Mythos Preview model with flagging the root-cause quirk. PoC code is public; as of June 22 no in-the-wild exploitation had been reported. Reporting on the fixed version conflicts: some coverage cites Squid 7.6, other coverage points to 7.7

Why It Matters
Squid sits as a shared egress and caching proxy in front of many enterprise and cloud networks, so a memory disclosure here crosses user and tenant boundaries — one user's request can leak another's session token inside the component meant to broker trust. The Heartbleed comparison is about shape, not scale: passive, hard-to-detect leakage with no crash and no log entry. The discovery method is the second story a 29-year-old bug that survived decades of human review was surfaced by a model.

Action for defenders
Inventory FortiSandbox appliances and confirm a fixed build, restrict WEB UI and management access to an admin segment, and hunt for the vendor/Defused indicators across the June window.

🏥 4. OpenAI ships GPT-5.5-Cyber for automated vulnerability finding and patching

Primary source: OpenAI
Reporting: Infosecurity Magazine

What Happened

OpenAI released GPT-5.5-Cyber under its Daybreak program, a model tuned to find vulnerabilities, validate exploitability, and generate patches in one workflow. OpenAI reported benchmark scores of 85.6% on CyberGym, 39.5% on ExploitGym, and 69.8% on SEC-bench Pro [VERIFY — vendor-reported]. Access is restricted to vetted defenders under added monitoring. OpenAI also updated its Codex Security plugin, which it says has scanned over 30 million commits across more than 30,000 codebases since March.

Why It Matters

This lands the same week Squidbleed showed a competing model finding a real 29-year-old bug, so the "AI finds and fixes vulnerabilities" claim now has production data points on both ends of the lifecycle. The consequence is asymmetry: the same exploit-generation capability that speeds your triage also lowers the cost of weaponizing a fresh disclosure, which compresses patch windows.

Action for defenders
Treat disclosure-to-exploit windows as shorter by default in your patch SLAs. If you run an AppSec program, evaluate gated defensive models against your current SAST and triage baseline rather than assuming parity.

🛡️ 5. Accenture takes majority stake in Dragos, buys runZero and NetRise in ~$4.1B OT push

Primary source: Accenture Newsroom
Reporting: SecurityWeek, Industrial Cyber

What Happened

Accenture announced it will take a majority stake in OT security firm Dragos (valued around $3.25B) and acquire runZero and NetRise outright, for a combined enterprise value near $4.1B [VERIFY — combined figure varies across sources]. The three together generate roughly $208M in annual recurring revenue, with the transactions expected to close in August or September 2026. Announced June 18, just outside the strict window, but included as the period's structurally significant M&A.

Why It Matters

A systems integrator buying its way to a majority stake in the leading independent OT threat-intel vendor changes the buying calculus for asset owners. OT security shifts from a best-of-breed product decision toward a bundled integrator engagement — a procurement-structure change, not a logo swap. For cloud security leads with IT/OT convergence in scope, the open question is whether Dragos's roadmap and vendor neutrality survive inside a services firm.

Action for defenders:
If you run Dragos, runZero, or NetRise, get contract-renewal and roadmap-continuity questions to your account team now. If you're evaluating OT monitoring, weigh integrator lock-in in the decision.

6. New 'Prinz Eugen' ransomware encrypts newest files first and skips the ransom note

Primary source: BleepingComputer
Reporting: SC Media

What happened: BleepingComputer reported a new Go-based ransomware, Prinz Eugen, that prioritizes the most recently modified files for encryption (alphabetical order on timestamp ties) and leaves no ransom note on the host. ThreatDown found hands-on-keyboard operators using legitimate RMM tooling and living-off-the-land binaries, with likely initial access through stolen RDP credentials and manual execution of a payload named servertool.exe. It uses ChaCha20-Poly1305 with Argon2id-derived keys and is not run as ransomware-as-a-service. At least five victims were identified, including Standard Bank, which refused a 1-BTC demand.

Why it matters: Encrypting recently modified files first inverts the usual recovery assumption: high-value in-flight working data is hit before bulk archives, so an early detect-and-kill response that would normally cap damage can still lose the data that matters most. No ransom note and no affiliate model means the usual leak-site and negotiation-portal indicators are absent, so detection has to come from RMM and LOLbin behavior. The RDP-credential entry point and living-off-the-land tradecraft are exactly the "acting like users" pattern Biggs describes as the new normal.

Action for defenders: Audit external RDP exposure and remote-access MFA, add detection for unexpected RMM tools and for servertool.exe executed by non-admin processes, and confirm backups capture active working directories at a tight enough interval to survive recent-files-first encryption.

🎯 Cloud Security Topic of the Week:

Data-first attacks broke the forensic assumption

The throughline of this week's news is the throughline of the episode: attackers don't need new capabilities, they need speed and reach, and AI gives them both. Biggs's sharpest observation is that the goal of the attack has changed. "Attacks predominantly used to be... encryption first, right?... Now it's data first, practically no encryption." That single shift rewrites the incident response playbook. When the objective is exfiltration rather than encryption, the question your lawyers, regulators, and contractually-armed partners will ask is not "is it back up?" but "what left, and whose was it?"

Here is the uncomfortable part, and the thing senior teams most often get wrong: forensics frequently cannot answer that question on its own. Windows artifacts show where an attacker moved and what they touched on a box, but they rarely prove what data went out the door. Without database query logging, without firewall egress that ties back to a specific endpoint, and without data classification done in advance, the honest answer to "was my data taken?" becomes "we don't know" — and that is the answer that ends customer relationships. The fix isn't a new product category. It's the unglamorous preparation work: a clean audit path from data to endpoint, and a first-pass classification of your CRMs and cloud storage so you can rule data in or out fast.[Listen to the full episode →]

Featured Experts This Week 🎤

Simon Biggs — Cyber Incident Response Specialist, Varonis
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

OAuth token (standing API access): A long-lived credential that grants an integration ongoing API access without re-authentication. In the Klue breach, a token created for an abandoned integration and never revoked became the initial access path into customer Salesforce tenants.
Data-first attack: An exfiltration-led intrusion with "practically no encryption," replacing the older encryption-first ransomware model (Biggs).
Living off the land (LOLbins): Attackers acting like legitimate users — using compromised credentials and built-in/legitimate tooling instead of dropping malware — which shrinks the detection opportunity. Seen this week in the Prinz Eugen ransomware tradecraft.
BloodHound / Metasploit / Kali Linux: Prepackaged offensive tool sets that historically lowered the barrier to entry. BloodHound maps Active Directory attack paths; Biggs uses these as the analogy for what AI now does on the fly.
Shadow AI: Users routing around sanctioned, locked-down models to less-secure alternatives — "a massive risk" (Biggs).
Prompt injection: Crafted input that makes an AI assistant carry out an unintended instruction. Varonis Threat Labs found a prompt-injection flaw in Microsoft Copilot.
Mythos: Anthropic's Claude Mythos Preview model, credited this week with helping surface the Squidbleed Squid proxy bug; named by Biggs as a strong example of AI-assisted vulnerability research.
Heap out-of-bounds write / over-read: Memory-safety bugs underlying both PixelSmash (write → RCE) and Squidbleed (over-read → secret disclosure).

This week's issue is sponsored by Varonis

AI Security Requires More Than Visibility. It Requires Control.

Security leaders are under pressure to enable AI innovation while managing a rapidly expanding attack surface across cloud, identity, and data layers. AI agents and copilots can introduce new access paths, automated high-impact actions, and accelerate threat timelines.

Varonis Atlas helps organizations secure AI end-to-end - from understanding usage and enforcing guardrails to detecting suspicious activity and reducing risk dynamically. watch the recording to learn how Varonis Atlas can help security teams operationalize AI security at scale.

💡Our Insights from this Practitioner 🔍

A single-guest conversation that doubles as a field report from someone who works real breaches. The throughline: AI changes the speed and reach of attacks, not their fundamental nature — and the defensive answer is preparation, not a new control category.

1: AI lowers the barrier and raises the speed — it is not a new class of attack

Biggs's front-line read cuts against the year's loudest marketing. AI increases scale and volume and lowers the skill required, but he is not seeing goals that were previously unachievable.

"But is AI driving something that's completely unseen? No, that's not what I'm seeing on the front line... I just think that it's lowering that technical barrier to entry. They're getting quicker. They're able to achieve better outcomes quicker during an attack." — Simon Biggs

Existing layered controls still work, and they matter more, because they slow attackers moving faster through the same paths.

2: Post-compromise automation now happens with no hands on the keyboard

The clearest AI signal Biggs sees is timing — automated action arrives in seconds.

"We're seeing sort of Microsoft Graph queries coming in like minutes, seconds after that token's been r- stolen via relay. That is unusual. Like that suggests there's no hands on the keyboard and, these AI kits are out there and are being used en masse, which is a big sea change..." — Simon Biggs

This is the practitioner mirror of the Klue story above: a stolen token is exercised almost immediately. Signature-based detection fails against ephemeral, briefly-lived phishing infrastructure on cloud and containerized platforms.

3: AI is the new "Metasploit/BloodHound" — it removes the operator's expertise

Ashish Rajan framed the shift directly: "it's almost like what Metasploit did for Script Kiddies. Is this something similar?" — Ashish Rajan

Biggs agreed, and took it further. Where chaining BloodHound output into Metasploit once required real expertise, models now pull the whole workflow together.

"So you don't actually have to be able to code, you don't actually have to really understand Active Directory... you can kinda get there without really any major technical skill, which is quite scary b- because that used to be, like a red team capability." — Simon Biggs

4: Attacks flipped from encryption-first to data-first

"Attacks predominantly used to be... encryption first, right?... Now it's data first, practically no encryption." — Simon Biggs

Response planning has to center on what data left, not what got encrypted. Biggs describes attackers taking a database schema, returning, and running a crafted query within minutes to pull credentials, payment contracts, and PII — behavior that used to be the preserve of nation-states and now shows up in ordinary ransom breaches.

5: Forensics can rarely tell you what was taken — and most teams overestimate it

"something I think people overestimate the ability of forensics to do is forensics to tell you what data's been taken... there's not many forensic artifacts that... will definitively tell you." — Simon Biggs

Windows forensics shows movement, not egress. Without database logging and per-endpoint firewall attribution, the answer to a lawyer's "did it leave?" is a hunch and lawyers don't notify on hunches. As Biggs puts it, "it's part of the incident response lifecycle, the preparation stage. That's where the battle is, is won or lost."

6: The defensive playbook is the same fundamentals — run the attacker's models against yourself

There are no magic new controls. Shadow-AI discovery, permissions hygiene, inventory and auditing, and automated response are the same problems as shadow IT and excessive permissions, extended to a new platform. The agent "is just an extension of the user" and should be audited as one.

"I'm an advocate of saying run BloodHound or run Metasploit. So... come from the point of view of the attacker and see what you find. I think it's the same. Like, run the same models, do the same thing to your environment and see what it finds." — Simon Biggs

7: AI-driven research widens the target surface, and same-day PoCs collapse patch windows

"from something getting released in the patch, people have working proof of concepts the same day. In a lot of cases we're seeing proof of concepts for things that aren't even patched yet or aren't even announced as vulnerabilities." — Simon Biggs

The economics have shifted with it: "You're not necessarily buying zero days for $50,000, $100,000. Somebody could get that in their bedroom. If they can afford the tokens." For perimeter assets, treat the disclosure-to-exploit window as effectively zero. PixelSmash and Squidbleed this week are the live examples.

8: Stolen data will be weaponized in new ways because AI lets attackers post-process at scale

"this information that's taken is gonna be weaponized in new and novel ways... what AI allows attackers to do is post-process data. So getting 10 terabytes of data is overwhelming... But now with AI, actually, they could be post-processing that data and... finding new and novel ways of monetizing it." — Simon Biggs

The liability follows. Third-party contracts increasingly require breach notification inside tight windows (often 72 hours, ahead of regulators like the ICO), and bigger partners "will come down heavy." An up-front classification pass on CRMs and cloud storage — which Biggs calls an "easy win" — lets you rule a terabyte out of scope instead of notifying everyone on a hunch.

Practical Takeaways for Cloud Security Leaders

Three things a senior team can act on: get a clean, queryable audit path from each critical data store back to an endpoint (logs that resolve to a real entity, not just an aggregator IP); do a first-pass data classification on CRMs and cloud storage so breach scope is answerable in minutes, not weeks; and run a dry-run IR exercise with someone offensive plus your blue team to find where the forensic trail dead-ends before an attacker does.

AppSec & DevSecOps Guidance

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The Control Plane Was the Target This Week. Your AI SOC Might Miss Why.

Ashish Rajan — Thu, 18 Jun 2026 13:49:39 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: The AI SOC is only as good as the data it reasons over (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

The pattern this week was uncomfortable: the systems an enterprise runs to see, operate, or protect everything else were the ones under attack. A pre-auth RCE chain landed in Splunk, the box your IR team would use to spot an intruder. FortiSandbox, the appliance that detonates files you don't trust, came under active exploitation. And Oracle PeopleSoft, the system of record for HR and payroll data, was exploited by ShinyHunters before any patch existed. When the exploited surface is the control plane, the blast radius isn't one host — it's the tooling you'd otherwise use to find and contain the others.

That makes this week's episode well-timed. Ashish Rajan sat down with Aqsa Taylor, Chief Security Evangelist at Exaforce (and formerly a product lead on Twistlock and Prisma Cloud), for a conversation about "vibe hunting" applying AI agents to threat hunting the way vibe coding applies them to development and what actually separates an AI SOC that earns trust from one that buries a real alert. Her throughline connects directly to the news: the AI is only as good as the data and context it reasons over. (Disclosure: Exaforce sponsored this episode.)[Listen to the episode]

⚡ TL;DR for Busy Readers

🚨 Oracle's patch arrived after the compromise had already begun. Review logs back to late May, not just the disclosure date.
A critical Splunk flaw exposed a surprising reality: deployment topology may matter more than patch status when assessing exposure.
FortiSandbox exploitation turned a security control into the attack surface itself.
Cisco SD-WAN and LiteSpeed joined CISA's KEV list, continuing a trend of attackers targeting management and control systems.
AI developer tooling became the latest supply-chain target, with compromised packages and fake AI assistants hunting for API keys.
🎯 The pattern matters more than the CVEs: attackers spent this week targeting the systems used to manage, detect, analyse, and govern everything else.
🤖 From this week's podcast: Aqsa Taylor (Exaforce) explains why AI SOC platforms fail without context, why "vibe hunting" is gaining traction, and why the future of detection depends more on the data model than the model itself.

📰 THIS WEEK'S TOP 5 SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. Oracle PeopleSoft zero-day (CVE-2026-35273) exploited by ShinyHunters before disclosure

Primary source: CISA KEV Catalog
Reporting: Oracle Security Alert · BleepingComputer · Help Net Security
Analysis: Rapid7

What Happened

Oracle issued an out-of-band alert for CVE-2026-35273 (CVSS 9.8), a server-side request forgery flaw in PeopleSoft PeopleTools 8.61 and 8.62 that is remotely exploitable without authentication and can lead to remote code execution. Mandiant and the Google Threat Intelligence Group attributed pre-disclosure exploitation to ShinyHunters (UNC6240), with activity running roughly May 27 to June 9 — before any patch existed. CISA added it to KEV on June 12 with a federal deadline of June 15. Stolen data was published on the ShinyHunters leak site.

Why It Matters

PeopleSoft is the system of record for HR and financial data at large institutions, so an unauthenticated SSRF-to-RCE converts directly into bulk exfiltration of the most regulated data an enterprise holds, plus a credential-harvesting foothold into federated identity systems. Exploitation predated the advisory by about two weeks, so log review back to late May matters more than racing the June 15 clock.

Action for defenders: Confirm PeopleTools is patched per Oracle's alert; pull web-tier and Integration Broker logs for SSRF patterns and anomalous outbound requests from late May onward, and treat any PeopleSoft-adjacent service-account credentials touched in that window as exposed.

🚨 2. Splunk Enterprise pre-auth RCE chain (CVE-2026-20253) is exposed by default on AWS

Primary source: Orca Security
Reporting: CyberSecurityNews · The Cyber Express

What Happened

Researchers disclosed CVE-2026-20253 (CVSS 9.8), an unauthenticated RCE chain in Splunk Enterprise 10 and later that abuses a misconfigured PostgreSQL sidecar. The sidecar isn't always enabled on-prem but runs by default in Splunk Enterprise on AWS, so cloud deployments are exposed out of the box. Exploitation allows file creation/destruction on the host, code execution inside the Splunk environment, and SSRF pivoting to internal resources. Splunk has released a fix.

Why It Matters

The SIEM holds credentials and network reach into nearly every system it monitors, so pre-auth RCE on Splunk is a detection-and-response decapitation — the attacker lands inside the tool your IR team would use to spot them. The default-on AWS exposure is the operative detail: "we didn't enable that service" is right on-prem and wrong in cloud, making deployment topology the thing to check first.

Action for defenders: Identify Splunk Enterprise 10+ instances on AWS, apply the fix, and until patched, restrict network reach to the PostgreSQL sidecar and keep the management interface on a hardened admin path

🛠 If you only do one thing this week: Take 30 minutes to map which of your security and detection tooling — SIEM, sandbox, log pipeline, AI gateway — is reachable from anything other than a hardened admin path, and on which platform. This week's Splunk and FortiSandbox flaws both turn on deployment topology, and the episode's core point is the same: the tool you'd use to catch the intruder is exactly what's being targeted.

☁️ 3. Fortinet FortiSandbox flaws under active exploitation

Primary source: CyberScoop
Reporting: SecurityWeek · The Hacker News
Analysis: CISA KEV

What Happened
Threat-intel firm Defused reported active exploitation of a pair of FortiSandbox flaws Fortinet disclosed earlier this year — 49 exploitation events from 11 IPs over six days, traced to nine countries. Reporting ties the activity to an OS-command-injection flaw (CVE-2026-39808) and a path-traversal flaw (CVE-2026-39813), the latter confirmed exploited June 15. Fortinet separately patched a WEB UI command-injection flaw, CVE-2026-25089 (CVSS 9.1); SOCRadar reported ~30,000 exposed Fortinet firewalls.

Why It Matters
FortiSandbox detonates files an organization doesn't trust, so code execution on the sandbox hands the attacker a privileged position inside the pipeline meant to contain malicious code — the containment tool becomes the execution environment. For hybrid estates feeding cloud-bound mail and file flows through FortiSandbox, a compromised appliance can poison verdicts and pass malware as clean into cloud workloads downstream.

🏥 4. CISA's June 15 KEV additions: LiteSpeed cPanel root escalation and a second Cisco SD-WAN flaw

Primary source: CISA Alert (June 15)
Reporting: The Hacker News · BleepingComputer · SecurityAffairs

What Happened

CISA added two actively exploited flaws to KEV on June 15. CVE-2026-54420 (CVSS 8.5) is a symlink-handling flaw in the LiteSpeed cPanel plugin (before v2.4.8; LiteSpeed WHM Plugin before 5.3.2.0) that lets a user with FTP or web-shell access escalate to root on shared-hosting servers running CloudLinux or CageFS; the federal deadline was June 18. The second add, CVE-2026-20262 in Cisco Catalyst SD-WAN Manager, is an arbitrary-file-write / path-traversal flaw distinct from the command-injection flaw (CVE-2026-20245) covered in the June 10 brief.

Why It Matters

A symlink-to-root flaw on CloudLinux/CageFS breaks the per-tenant isolation shared hosting sells as its core control, so one tenant with a web shell reaches every other tenant on the box — a multi-tenant blast radius on the hosting tier many SaaS and agency workloads still run on. A second Cisco SD-WAN Manager flaw in two weeks means the controller is under sustained probing; reopen that item if you closed it after June 10.

Action for defenders
Upgrade the LiteSpeed WHM/cPanel plugin and review hosting-server logs for symlink-abuse indicators; for Cisco SD-WAN Manager, confirm both CVE-2026-20245 and CVE-2026-20262 remediations and restrict management-plane access to a hardened jump path.

🛡️ 5. Supply-chain attacks shift to AI developer tooling: Mastra npm namespace and fake JetBrains plugins

Primary source: The Hacker News
Reporting: Help Net Security

What Happened

Two near-simultaneous supply-chain compromises aimed at AI development tooling. Roughly 144 npm packages under the Mastra namespace were compromised after a single account mass-published 140-plus malicious packages within a short window on June 17. Separately, a coordinated JetBrains Marketplace campaign published at least 15 malicious plugins posing as AI coding assistants that exfiltrate AI-provider API keys to attacker-controlled servers.

Why It Matters

The credential these campaigns steal is the AI-provider API key, now a billing-and-data primitive — a leaked key buys model access on the victim's account and the data flowing through it. Targeting the AI-coding-assistant supply chain means the malicious code lands inside the developer's agentic toolchain, which already has filesystem, repo, and often cloud-credential reach. It's the news-side mirror of what Aqsa Taylor describes below: attacks that ride GitHub and package registries and look like normal developer activity until you have repo-layer context.

Action for defenders:
Inventory Mastra packages and AI-coding-assistant IDE plugins across engineering, rotate any AI-provider API keys reachable from dev machines or CI in the June 17 window, and add AI-provider keys to the secret-scanning and short-TTL policies you apply to cloud credentials.

🛡️ 6. Databricks acquires Panther Labs, consolidating the security-data layer for AI-era detection

Primary source: Tech Startups
Reporting: Infosecurity Magazine

What happened: Databricks agreed to acquire Panther Labs, a cloud-native security-data and detection platform whose customers include Anthropic. Reporting frames it as Databricks' third cybersecurity acquisition and a move to compete with CrowdStrike and Cisco-owned Splunk as enterprises rebuild detection for AI-driven threats. Financial terms were not disclosed.

Why it matters: A data-platform vendor buying a SIEM-class detection layer points at where security operations is consolidating — onto the same lakehouse that already holds the enterprise's analytics data. For architects it reframes a procurement question (which SIEM) into a data-gravity question: if detection moves to where the data already lives, tool-selection independence narrows toward whoever owns the lakehouse. It rhymes with the episode's argument that the AI SOC is defined by its data model, not its label.

Action for defenders: If Panther or Databricks is in your stack, map current detection-content and data-residency dependencies now, so a post-acquisition consolidation doesn't quietly relocate where your security telemetry lives or who governs access to it.

🎯 Cloud Security Topic of the Week:

The AI SOC is only as good as the data it reasons over.

This week's news kept hitting the tools security teams rely on — the SIEM, the sandbox, the package registry developers trust — and that is exactly the surface Aqsa Taylor's argument is about. The pitch for "AI SOC" is everywhere; by her count there are more than 54 startups in the category, plus every legacy SIEM and SOAR rebadging into it. Her test for separating signal from marketing isn't the model but the data: what config, identity, code, and posture context does the platform fold in before it tells you what's a false positive? As she put it, "AI can help, but it can also hurt without the right context." The same week attackers compromised npm and JetBrains plugins that look like ordinary developer activity, that's not abstract — it's the difference between a tool that catches the anomaly and one that waves it through. [Listen to the full episode →]

Featured Experts This Week 🎤

Aqsa Taylor — Chief Security Evangelist, Exaforce
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Vibe hunting — "If you're using AI for code development, it's vibe coding. If you're using AI for threat hunting in SOC, vibe hunting." The analyst's skill stays; AI makes the hunt faster and higher-confidence.
AI SOC — A contested umbrella label. SIEM and SOAR vendors now market themselves as AI SOC too, so the category name tells you little; the data the AI reasons over tells you everything.
The four pillars — Detection, investigation, triage, response. Triage is the commoditized layer; the differentiated value is level-two investigation and, carefully, response.
Semantic / real-time knowledge graph — A "living graph" fusing events with configuration, identity, code, and posture context, retaining business-context exceptions over time.
Peer-group baselining — Judging a user's behavior against their team's baseline, not only their own.
MFA fatigue / "spray and pray" — Flooding a user with auth attempts so they approve one MFA prompt; response is session/token revocation and blast-radius containment.
ITDR / ISPM — Identity threat detection and response / identity security posture management.
CNAPP — Cloud-native application protection platform, where code-repo and posture context typically live before it reaches the SOC.

This week's issue is sponsored by Varonis

AI Security Requires More Than Visibility. It Requires Control.

💡Our Insights from this Practitioner 🔍

1. "Vibe hunting" came from the SOC floor, not the marketing team

Aqsa is explicit that the term originated with her company's own MDR team during a live hunt, then stuck because it mirrored vibe coding. The substance underneath is automation agents that update detection logic continuously as indicators publish, instead of an analyst manually chasing blogs and Substacks.

"If you're using AI for code development, it's vibe coding. If you're using AI for threat hunting in SOC, vibe hunting." — Aqsa Taylor

Point agents at trusted IOC sources so detection logic updates as indicators land, while checking the environment's exposure window from configuration and posture data — not only from inbound events.

2. AI without context can hurt, not just help

The sharpest take is a warning, not a pitch. An AI layer that just wraps event severity can bury a real alert among false positives, because it lacks the context to judge what matters.

"AI can help, but it can also hurt without the right context." — Aqsa Taylor

Her example is the HackerBot Claw campaign: not a code vulnerability but malicious pull requests, where a payload manipulated Claude's auto-merge instructions and altered a README. PR-change requests aren't something traditional scanners alert on — the same repo-layer blind spot this week's Mastra and JetBrains compromises exploited.

3. Fix the data model before you trust the agents

Trust in AI agents depends on the underlying data model, not the model's raw intelligence. A more powerful frontier model doesn't remove the need for context.

"even before we move into AI, I think the data model on which the AI runs is so important to be able to then trust the AI agents" — Aqsa Taylor

She contrasts a static prompt-with-Claude approach — which works only on the data you feed it — against a "proactive model" where the data is a living graph that surfaces exposure on its own.

4. The hardest detections look exactly like normal activity

The detection problem worth solving is the legitimate-looking behavior. Aqsa's example is a North Korean "fake employee" with valid access who exfiltrates by copying a sensitive file and sharing the copy.

"They could copy the contents and then share the copied file with external, and you would not see that as a shared event because you're seeing the main file." — Aqsa Taylor

Catching that requires SaaS-layer visibility across Google Workspace, GitHub, Okta, and Slack, plus peer-group baselining: comparing a new hire's behavior to their team's, not just their own short history.

5. "AI SOC" is a crowded label — interrogate the data, not the badge

When Ashish notes buyers face 50-plus vendors, Aqsa's answer is to stop selecting on the label.

"there's over 54 in the startup world ... in AI SOC. And then I'm not even counting all the traditional platforms who have pivoted to AI SOC messaging more recently." — Aqsa Taylor

The buying question to ask instead: what factors — config, identity, code, location — does the platform weigh when it reduces false positives, and is it proactive enough to run its own detections rather than waiting on upstream providers?

6. AI doesn't change the threats; it changes the volume and the timeline

Across the four pillars, most platforms only do triage — enriching upstream events. The real value is reaching the judgment of an experienced level-two analyst.

"where defenders really need help with AI and where AI can give a lot more is level two threat investigation." — Aqsa Taylor

She expects 2026 to be when teams move past level-one and level-two triage into investigation and, carefully, response — but only after the first three pillars build trust. Response agents are real (she cites customer testimonials recorded during an RSA panel), but they earn autonomy.

7. Response works when one platform owns the whole kill chain

The response pillar becomes viable when the foundation pillars feed it — so the platform runs detection, identity mapping, blast-radius, and containment without hopping between ITDR, threat-hunting, and SOAR tools.

"we saw a credential stuffing attempt where there were like 390 authentication attempts across 14 accounts." — Aqsa Taylor

In that MFA-fatigue case, tying the detection to identity risk (who clicked phishing before), impact radius (which sensitive files the account can reach), and recent sharing activity — then revoking sessions — collapses a multi-tool workflow into one chain.

8. Don't build it yourself — equip your defenders instead

Both host and guest land on the same build-versus-buy conclusion. Ashish frames the trap from a CISO's chair:

"I am actually employing people to build a product in my own company. Is that what I'm going towards?" — Ashish Rajan

Aqsa agrees: DIY with Claude can help with smaller scripts, but it doesn't remove the context, accuracy, and confidence-scoring burden — "it's almost like you're changing the effort, but you're still putting effort." Her recommended starting point: begin with level-one triage on top of your existing SIEM using a dedicated platform, and "start from data" by asking what the platform weighs before you trust its verdicts.

"You need to make sure that you're equipping your defenders, your team, with the same advantage that the attackers have." — Aqsa Taylor

Practical Takeaways for Cloud Security Leaders

Map which security tooling (SIEM, sandbox, AI gateway) is reachable outside a hardened admin path, and on which platform — this week's flaws turn on topology.
Rotate AI-provider API keys reachable from dev machines and CI, and scan for them like cloud credentials.
When evaluating an AI SOC, ask what data (config, identity, code, location) it weighs — not whether it says "AI."
Start with level-one triage on your existing SIEM; treat the full four-pillar model as the destination, not the first project.

AppSec & DevSecOps Guidance

CISA Known Exploited Vulnerabilities Catalog — authoritative exploitation status for this week's CVEs
Oracle Security Alert — PeopleSoft CVE-2026-35273
Rapid7 — Active exploitation of the PeopleSoft zero-day
Orca Security — Splunk Enterprise RCE (CVE-2026-20253)
CISA Alert — June 15 KEV additions
CyberScoop — FortiSandbox exploitation

Podcast Episode

Cloud Security Podcast : Episode with Aqsa

Question for you? (Reply to this email)

🤔 When did a "normal-looking" action — a copied file, a pull request — turn out to be the incident? What gave it away?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

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🚨 Three Exploited Flaws, No Patch Coming - Murali Rathinasamy on Why Micro-Segmentation Is the Destination, Not the Project

Ashish Rajan — Wed, 10 Jun 2026 23:30:32 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Segmentation for the week the patches didn't come — hybrid mesh firewall and staged micro-segmentation (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

This week's Cloud Security Newsletter topic: Segmentation for the week the patches didn't come — hybrid mesh firewall and staged micro-segmentation.

No single breach carried this week. The pattern did: a cluster of actively exploited vulnerabilities in control-plane infrastructure, several added to CISA's KEV catalog within 48 hours, and vendors increasingly responding with ACLs and config changes instead of code. If your triage starts with "is there a patch," this was the week that question stopped working.

That makes the timing of this week's episode useful. Ashish Rajan sat down with Murali Rathinasamy, Senior Director of Product at Cisco, for a conversation about hybrid mesh firewall: what the category actually is, where it differs from CNAPP, and a staged approach to micro-segmentation that starts with blocking ports your own traffic data says you never use. (Disclosure: Cisco sponsored this episode. This week's news also includes an actively exploited Cisco SD-WAN flaw, covered on its merits below.)[Listen to the episode]

⚡ TL;DR for Busy Readers

Check Point VPN auth bypass (CVE-2026-50751, CVSS 9.3) ran exploited for about a month before the June 8 hotfix, with a Qilin ransomware affiliate among the users. Apply the hotfix and retire IKEv1 now.
Cisco SD-WAN Manager zero-day (CVE-2026-20245) is exploited with no patch or mitigation available. Restrict management-plane access to a hardened jump path and rotate netadmin credentials today.
LiteLLM RCE (CVE-2026-42271) is the first KEV-listed AI-gateway flaw. Patch to ≥1.83.7, block the /mcp-rest/test/* endpoints, and rotate every model-provider key the proxy held.
Arista EOS tunnel flaw (CVE-2026-7473): exploited, no patch planned, and it bypasses the VXLAN/GRE segmentation your fabric design assumes. ACLs are the permanent fix.
From the episode: treat full micro-segmentation as the destination, not the project. Start agentless, block what observed traffic shows is unused (SMB 445 first), and save agents for crown jewels.

📰 THIS WEEK'S TOP 5 SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. LiteLLM RCE chain exploited in the wild; CISA adds first AI-gateway flaw to KEV

Primary source: CISA KEV Catalog
Reporting: The Hacker News · Help Net Security
Analysis: Horizon3.ai

What Happened

CISA added CVE-2026-42271, a command-injection flaw in BerriAI's LiteLLM proxy (CVSS 8.7), to the KEV catalog on June 8, citing active exploitation. Two MCP-server preview endpoints accepted a full server config in the request body and spawned the supplied command as a subprocess on the proxy host. Horizon3.ai chained it with CVE-2026-48710, a Starlette host-header validation bypass, to reach unauthenticated RCE. Fixed in LiteLLM 1.83.7 and Starlette 1.0.1.

Why It Matters

The LLM proxy is where enterprises now concentrate model-provider API keys, internal endpoint credentials, and the routing config for every AI app behind it. A shell on that host means the blast radius is every model credential the gateway holds, not one application. This is the first KEV-listed AI-gateway RCE, and it reframes the LLM proxy as a tier-0 identity asset that belongs in the same patch SLA as a domain controller.

Action for defenders: Inventory LiteLLM deployments, confirm version ≥1.83.7, block the two /mcp-rest/test/* endpoints at the reverse proxy if you cannot patch immediately, then rotate any model-provider keys the proxy stored.

🚨 2. Check Point VPN auth bypass exploited for a month; Qilin affiliate among the users

Primary source: Check Point advisory
Reporting: BleepingComputer · SecurityWeek
Analysis: Rapid7

What Happened

Check Point disclosed CVE-2026-50751 on June 8, a CVSS 9.3 logic flaw in certificate validation that lets a remote, unauthenticated attacker establish a Remote Access or Mobile Access VPN session without a valid password. It affects deployments using the deprecated IKEv1 protocol and Spark firewalls. Check Point traces exploitation to May 7 (vendor's own assessment) and ties it to at least one Qilin ransomware intrusion that used Rclone for exfiltration. CISA set a June 11 federal KEV deadline. A related flaw, CVE-2026-50752, affects IKEv1 site-to-site certificate validation.

Why It Matters

Roughly a month of in-the-wild use before a fix existed, and the entry point is the appliance fronting the corporate network. A ransomware affiliate gets the same network position as an authenticated remote employee, minus the credential. The IKEv1 dependency makes this a configuration-debt story: the exposed set is everyone who never migrated to IKEv2, so remediation is an architecture audit, not just a hotfix.

Action for defenders: Apply the hotfix. If you cannot, switch Remote Access VPN to IKEv2-only, make machine-certificate authentication mandatory, enable IPS, and hunt logs for the published VPS-hosted source IPs.

☁️ 3. Cisco Catalyst SD-WAN Manager zero-day exploited; no patch available

Primary source: Cisco Security Advisory
Reporting: Help Net Security · BleepingComputer · The Hacker News

What Happened
Cisco confirmed active exploitation of CVE-2026-20245 (CVSS 7.8) in Catalyst SD-WAN Manager, a command-injection flaw that lets an authenticated attacker with netadmin privileges execute commands as root by uploading a crafted file. Google Mandiant reported it, and Cisco observed attackers pushing configuration changes down to edge devices. No patch or mitigation is available. The netadmin role can be obtained via stolen credentials or by chaining earlier SD-WAN flaws (CVE-2026-20182, CVE-2026-20127).

Why It Matters
SD-WAN Manager is the control plane for branch and cloud-edge connectivity. Root on the manager is not one box; it is the ability to rewrite routing and policy on every managed edge device, which is pre-positioning capability rather than a single-host compromise. With no patch on offer, the defensive question shifts from "when do we deploy the fix" to "who can reach the manager's CLI at all."

Action for defenders
Restrict management-plane access to a hardened jump path, audit netadmin accounts and rotate their credentials, and review edge-device config history for unexpected pushes.

🛠 If you only do one thing this week: List which of the four exploited access-broker products you run — Check Point IKEv1 VPN, Catalyst SD-WAN Manager, Arista tunnel-decap endpoints, LiteLLM — and for each one write down either the patch version deployed or the named compensating control and its owner. Thirty minutes, and it converts this week's thesis (the patch isn't coming; compensate at the network layer) into a checklist your team can act on.

🏥 4. ServiceNow discloses unauthenticated API flaw used to query customer instance data

Primary source: BleepingComputer
Reporting: Hackread

What Happened

ServiceNow disclosed on June 9 that attackers exploited an unauthenticated-access flaw in one of its API endpoints to run queries against customer instances. Observed activity traces to June 2–3; ServiceNow remediated hosted instances on June 5 with no customer action required. Community reporting points to a Scripted REST Resource deployed with requires_authentication=false. The bulletin centers impact on the Australia platform release and older releases with certain config changes.

Why It Matters

This is a multi-tenant SaaS auth bypass in the provider's own platform code, not a customer misconfiguration, so "harden your instance" would not have prevented it. Because the vendor fixed it server-side, most affected customers will see no signal unless they go looking. "The provider patched it for you" and "you have no exposure" are different claims; log review is the only way to know which one applies to you.

Action for defenders
Pull instance logs for requests to the affected endpoint and the published indicator IP (51.159.98.241) across the June 2–5 window, and confirm with your account team whether your release was in the impacted set.

🛡️ 5. Google patches fifth actively exploited Chrome zero-day of the year

Primary source: CISA KEV
Reporting: Help Net Security · BleepingComputer · The Hacker News

What Happened

Google patched CVE-2026-11645, an out-of-bounds read/write in the V8 JavaScript engine allowing arbitrary code execution in the browser sandbox via a crafted HTML page. An exploit exists in the wild; CISA added it to KEV on June 9. It is the fifth actively exploited Chrome zero-day of 2026 and, because the bug is in Chromium, it also affects Edge, Opera, and other Chromium-based applications. Fixed in Chrome 149.0.7827.102/.103.

Why It Matters

The exposure is not just user browsers. Chromium is embedded across cloud workloads, headless automation, and CI rendering, so "patch the browser" understates where the engine runs. The instances that stay exploitable after every desktop updates are the build pipelines and serverless functions shipping a bundled Chromium nobody patches on Google's cadence.

Action for defenders:
Force-update managed Chrome/Edge fleets, then inventory container images and Lambda/Cloud Run layers that bundle Chromium or Puppeteer and rebuild against the patched version.

🎯 Cloud Security Topic of the Week:

Segmentation for the week the patches didn't come!

Three of this week's exploited flaws shipped with no patch, and the vendor guidance in each case was a network-layer control: ACLs on Arista fabric, access restriction on SD-WAN Manager, protocol migration on Check Point. That is compensating-control work, and it lands on whoever owns segmentation. Murali Rathinasamy's argument on the podcast is that this work stalls for a predictable reason. As he put it: "Micro-segmentation always stalls in the phase of how do I know what I need to go protect, and what policy should I go use?" His staged answer, starting from observed traffic and agentless enforcement rather than a multi-year agent rollout, is the practical core of this edition's insights section. [Listen to the full episode →]

Featured Experts This Week 🎤

Murali Rathinasamy - Senior Director of Product, Cisco
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Hybrid mesh firewall — As the guest describes it: the evolution after perimeter and next-gen firewalls, recognizing that enterprise networks now span data centers and clouds. A distributed set of enforcement points (physical, virtual, container, cloud-native; inline and out-of-band) managed uniformly: "uniformly managing this entire distribution of inline and out-of-band detection and threat capabilities." A category, not a single product.
Micro-segmentation — Locking down communication at the level of individual VMs, containers, and devices. The guest's framing: the end state where "every individual VM, every individual container, every individual device is really locked down."
North-south vs east-west — Perimeter traffic (inspection, decryption, DLP, WAF-style inbound protection) versus traffic between internal workloads (segmentation territory).
Compensating control — A control that reduces exploitability when fixing the flaw itself isn't possible: a virtual-patch rule, step-up MFA in front of a vulnerable app, or an ACL where no patch is coming. The connective tissue between this week's news and the episode.
KEV (Known Exploited Vulnerabilities) catalog — CISA's list of flaws with confirmed active exploitation, carrying federal remediation deadlines. Four of this week's stories involve KEV additions inside one 48-hour stretch.
Blue-green upgrade — Standing up the new version alongside the old and cutting traffic over, eliminating upgrade downtime. The operational expectation cloud teams now hold firewalls to, per the RCSI example.
IKEv1 — The deprecated IPsec key-exchange protocol whose continued use defines the exposed population for CVE-2026-50751.

This week's issue is sponsored by Varonis

AI Security Requires More Than Visibility. It Requires Control.

💡Our Insights from this Practitioner 🔍

1. Cloud and on-prem are one network; treating them as islands is the root failure

The tooling split, CNAPP and CSPM for cloud and appliance firewalls for on-prem, forces a divide that the org chart doesn't actually have. The same network security team usually owns both halves.

"What we've seen in the industry is the challenge is that customers will often think about their cloud security as one island in one pocket of the world, but then their on-prem is a different pocket of the world. Really though no enterprise thinks about them separately. It's all one hybrid network, and wherever the application are and wherever the users are, they wanna pro- uh, protect that in totality." — Murali Rathinasamy

The practical version of this insight: asking an on-prem firewall admin to also master CNAPP, CSPM, and per-cloud native tooling for only half their environment is a skills tax most teams can't pay. Royal College of Surgeons in Ireland (RCSI) is the worked example below.

2. AI agents look like users but don't behave like users

Murali's sharpest AI observation is about identity, not models. Agents inherit a user's identity and then access things in patterns no human baseline predicts.

"You now have new applications because at the end of the day, everyone is now an application developer because they can go and create their own applications. These agents now can sort of look like a user, but they're doing things in ways that users don't do. So even traditional behavioral analysis tools may not work because you'll see user Murali traditionally uses this application, and now his agents are going all over the place." — Murali Rathinasamy

This pairs directly with the LiteLLM story above. If agents defeat behavioral baselines and the AI gateway concentrates credentials, the controls that still work are the structural ones: segmentation that limits what an agent can reach, and inspection at the choke point between workload and model.

3. The single-cloud security story collapses at enterprise reality

A former AWS product manager arguing against all-in on cloud-native firewalling carries some weight:

"As a former AWS product manager, I would tell you that I would've said the exact same thing. 'Hey, you're in the AWS ecosystem. We have, we've got the best in class services. Go and use ours entirely.' However, the reality for all the enterprises I work with, literally all of the enterprises that I work with is none of them are one cloud provider. A, none of them are one cloud provider. All of them have at least two cloud providers, and then B, they all have on-prem deployments as well." — Murali Rathinasamy

His decision lens is ownership: if a centralized security team is responsible for every workload everywhere, per-cloud native tooling means re-skilling that team on each platform and stitching policy visibility across VPCs, Azure, and GCP by hand. Where cloud providers win, he concedes, is scalability; where customers tell him they're not there yet is security feature depth.

4. RCSI: virtual firewalls in the cloud fail on operations, not security

RCSI, a 200-year-old institution , ran Cisco FTD on-prem and lifted virtual firewalls into the cloud, then hit route-table plumbing, transit gateway config, self-managed scaling, and upgrade downtime.

"What RCSI realized was like, hey, this is just not a scalable model. Like, any time I need to do a software upgrade for the firewall, I have to go take downtime? My cloud application teams are like, 'That's crazy.' Like, no cloud team really thinks about downtime to do an upgrade. It's always a blue-green upgrade." — Murali Rathinasamy

The fix wasn't a different security product; it was operating the same firewall like a cloud service (orchestrated deployment, auto-scaling, blue-green upgrades via Multicloud Defense). The lesson generalizes beyond Cisco: when network security tooling can't match the operational bar cloud teams hold everything else to, the security tool loses the argument.

5. Micro-segmentation stalls on "what do I protect?" — make it the destination, not the project

Ashish's framing set up the episode's most useful exchange:

"Micro-segmentation. It's probably the most spoken, yet least implemented space of the industry." — Ashish Rajan

Murali's response is the staged model. First, recognize you've already started: a perimeter firewall is one segment, and most enterprises already firewall crown jewels or cloud boundaries. Second, use observed traffic to cut obvious attack surface agentlessly, without touching applications:

"While we, you know, uh, enterprises talk quite a bit about segmentation and micro-segmentation, at the end of the day, micro-segmentation always stalls in the phase of how do I know what I need to go protect, and what policy should I go use?" — Murali Rathinasamy

His concrete examples: Windows Server SMB (445) is among the most exploited ports in a data center and almost never legitimately used; block it. SSH should only originate from jump hosts; block 22 from everywhere else. A large healthcare provider in California (unnamed) deployed micro-segmentation agents only on its EMR, the crown jewels, and used existing Cisco firewalls agentlessly for everything else, cutting the lateral path from systems like payroll to the EMR.

"Worry about the north star of true micro-segmentation where every individual VM, every individual container, every individual device is really locked down. Think of that as the destination, don't think of that as the journey." — Murali Rathinasamy

6. AI doesn't change the threats; it changes the volume and the timeline

"To me, like the AI world is really more about, it's not a different set of threats. It, it's the same sort of threats, it's just a much higher volume of those threats on a much shorter timeline, right?" — Murali Rathinasamy

That reframe has a budget implication: the answer to AI-era threats is mostly not new threat categories or new tools, it's shrinking time-to-control on the ones you have. On the prompt layer specifically, his point is architectural: there is already a firewall between your users or workloads and the LLM, so that's where inspection belongs, including prompt-injection detection and blocking responses that leak what they shouldn't.

7. Compensating controls are the answer to software you can't patch

The quote that could have been written about this week's news: "Cisco is, uh, working very closely with Mythos on being able to identify vulnerabilities in our own software to patch them very quickly, and we're realizing that Mythos is the new reality in the world of all CISOs and CIOs, CTOs have known that all software is gonna have vulnerabilities. It's really about how do you close those vu- vulnerabilities quickly and use compensating controls to make sure that they're not, uh, exploited w- before you can kind of fix it." — Murali Rathinasamy

For COTS applications and legacy systems (his example: MRI machines on Windows XP-era software), patching is not in your control. The realistic play is a virtual-patch rule for a Log4j-style flaw, or step-up MFA in front of an app you know is vulnerable, while the vendor or app team builds the fix. Set against Arista's "no patch planned" and Cisco SD-WAN's "no patch available," this stopped being a vendor talking point and became the week's operating reality.

Practical Takeaways for Cloud Security Leaders

Embed AppSec in the engineering loop not after it. If your team is still receiving code and generating tickets, you are operating legacy AppSec. Start conversations with engineering about where security testing can run inside the CI/CD pipeline itself.
Shift from false positive tolerance to true positive precision. A 90% false positive rate is a testing architecture problem, not a signal problem. Test inside the development environment to eliminate WAF and CDN interference.
Treat AI agents as service identities, not applications. Apply your IAM governance framework to every agentic workload before production deployment. Default permissions are almost always too broad. BYOSA on Vertex AI; scoped service accounts everywhere else.
Audit your CI/CD supply chain assumptions today. Pin GitHub Actions to full commit SHAs, not floating tags. Assume any runner that executed Trivy, LiteLLM, Telnyx, or Axios between March 19–31 is compromised until proven otherwise.

Position security as a business enabler for AI transformation. CISOs who approach AI as purely a risk management exercise will be sidelined. Those who help engineering teams ship AI features securely and at speed will own one of the most important mandates in their organization.

AppSec & DevSecOps Guidance

CISA Known Exploited Vulnerabilities Catalog — authoritative exploitation status for this week's CVEs
Cisco Security Advisory — SD-WAN Manager CVE-2026-20245
Check Point hotfix advisory — IKEv1 VPN flaws
Arista Security Advisory 0137 — EOS tunnel decapsulation
Horizon3.ai — LiteLLM RCE chain analysis
Rapid7 — Check Point VPN zero-day analysis

Podcast Episode

Cloud Security Podcast Episode with Murali

Question for you? (Reply to this email)

🤔 Which of your controls exist because a patch never shipped — and would you know if one quietly stopped working?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

📬 Want weekly expert takes on AI & Cloud Security? [Subscribe here]”

We would love to hear from you📢 for a feature or topic request or if you would like to sponsor an edition of Cloud Security Newsletter.

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Peace!

This issue is sponsored by CheckPoint

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Cloud-Credential Worm Hit Red Hat & DoorDash's approach to Security at the Speed of Engineering

Ashish Rajan — Wed, 03 Jun 2026 23:25:19 +0000

This week's Cloud Security Newsletter topic: Security at the Speed of Engineering — Guardrails, Not Gates (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

No single breach defined the week. What defined it was reuse and speed: offensive tooling getting mass-produced and recycled faster than defenders — or governments — can measure it, landing squarely on the identity edge and the container boundary. A credential-stealing worm rode a maintainer’s GitHub account straight past code review into Red Hat’s npm namespace. A three-year-old container-escape bug earned a fresh KEV listing the day after in-the-wild exploitation was reported. And Sophos pulled apart a ransomware crew’s AI-coordinated lab built to test malware against three named commercial EDRs.

This week’s conversation features Nick Reva, who runs global security engineering at DoorDash, and Shivani Doke, a GRC engineer, recorded live in front of a San Francisco audience and hosted by Ashish Rajan. The thread running through both the news and the episode: AI lowers the barrier on both sides of the fight, so the durable controls are the ones embedded in the development lifecycle and validated continuously, with humans in the loop only at the decisions that matter. [Listen to the episode]

⚡ TL;DR for Busy Readers

- Miasma worm hit Red Hat’s npm namespace. At least 32 
@redhat-cloud-services releases tampered to steal AWS/GCP/Azure 
keys, Kubernetes service-account tokens, and Vault tokens at 
install time. Rotate cloud and CI secrets on any host that 
pulled affected versions since June 1.

- Two KEV deadlines this week. PAN-OS GlobalProtect auth bypass
 (CVE-2026-0257, mitigate by June 1) and Linux cgroups v1 
container escape (CVE-2022-0492, due June 5). Triage by 
config and node image, not CVSS.

- AI-built EDR-evasion lab surfaced. Sophos documented a 
crew using AI agents to iterate payloads against Sophos, 
CrowdStrike, and Microsoft Defender. Treat EDR as a detection 
layer to validate, not trust.

- Prompt-injection metrics don’t compare across labs. No two 
of the four major AI providers measure injection resistance 
the same way. Demand a vendor’s test methodology before 
deploying agents in sensitive workflows.

- Reva + Doke’s frame: run security at engineering speed. 
Embed small security pods in product teams, surface findings 
on the pull request, triage AI-generated bug-bounty noise with 
AI, and reserve humans for the novel work.

THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. Miasma Supply-Chain Worm Compromises @redhat-cloud-services npm Packages and Harvests Cloud Credentials

Primary source: Wiz Research
Reporting: BleepingComputer · Cybersecurity Dive · Aikido
Analysis: Palo Alto Unit 42

What happened: Wiz Research reported on June 1 that at least 32 package releases under the @redhat-cloud-services npm namespace carried unauthorized modifications that didn’t match their source repositories. The payload is “Miasma,” a new variant of the Mini Shai-Hulud credential-stealing worm whose code TeamPCP previously open-sourced. A compromised Red Hat employee GitHub account was used to push malicious orphan commits to two RedHatInsights repositories, bypassing code review; the tampered packages ran obfuscated preinstall scripts at install time, attempting to collect GitHub Actions tokens; AWS, Google Cloud, and Azure credentials; HashiCorp Vault tokens; Kubernetes service-account tokens and kubeconfig files; npm and PyPI publishing tokens; SSH keys; Docker registry credentials; and .env files. Affected packages average ~80,000 weekly downloads. Wiz called the TeamPCP link TTP overlap, not definitive attribution.

Why it matters: Once Shai-Hulud was open-sourced, the worm became commodity code anyone can fork — so “is this TeamPCP?” stops being the useful question. The intrusion rode a maintainer’s account and orphan commits past code review into a trusted vendor namespace, and the theft executes at npm install on developer and CI hosts, not at runtime. The credential target list reads like a cloud-platform team’s secret store.

2. CISA Adds PAN-OS GlobalProtect Authentication Bypass (CVE-2026-0257) to KEV — June 1 Federal Deadline

Primary source: CISA KEV Catalog · Rapid7
Reporting: BleepingComputer · The Hacker News
Vendor advisory: Palo Alto Networks

What happened: CISA added CVE-2026-0257, an authentication bypass in PAN-OS and Prisma Access GlobalProtect, to the KEV catalog on May 29 with a June 1 federal mitigation deadline. Rapid7 MDR observed exploitation across multiple customers, earliest activity on May 17, and reported no successful lateral movement from affected devices. The flaw lets an unauthenticated remote attacker establish a VPN connection through the GlobalProtect gateway when authentication-override cookies are enabled alongside a specific certificate configuration.

Why it matters: The bypass turns the remote-access gateway — the control meant to gate who reaches the network — into an unauthenticated entry path. Exposure depends on a specific configuration, not mere presence of the product, so CVSS-only triage won’t tell a team which appliances are actually reachable. The disclosure-to-KEV interval was short (exploitation observed May 17, listed May 29), ahead of normal monthly patch rhythms.

3. CISA Adds Linux Kernel cgroups Container-Escape Flaw (CVE-2022-0492) to KEV — Due June 5

Primary source: CISA alert
Reporting: SecurityWeek
Analysis: Palo Alto Unit 42 · Aqua Security

What happened: On June 2, CISA added CVE-2022-0492 — a privilege-escalation flaw in the Linux kernel’s cgroups v1 control-groups feature — to the KEV catalog with a June 5 federal remediation deadline (the same alert added Android Framework zero-day CVE-2025-48595). The cgroups bug lets a process modify the release_agent file, which executes as root in the host namespace; combined with a new user namespace, it allows container escape to the host. Only cgroups v1 is affected. Technical details were published roughly three years ago, but in-the-wild exploitation was reported only recently — one day before CISA’s alert.

Why it matters: A three-year-old kernel flaw earning a fresh KEV listing is the week’s quiet but pointed item: the payoff is escape from a container to its host, which on a shared Kubernetes node means crossing the tenancy boundary teams treat as a containment line. The operative signal is recency-of-exploitation, not recency-of-disclosure.

4. Sophos Uncovers an AI-Coordinated Lab Built to Test Malware Against Named EDRs

Primary source: Sophos research (via BleepingComputer)
Reporting: Help Net Security · CyberSecurityNews

What happened: Sophos disclosed a threat actor’s Git repository containing an automated Active Directory discovery panel and a lab that iteratively develops and tests malware against Sophos, CrowdStrike, and Microsoft Defender EDR agents. Per Sophos, the framework used multiple AI agents coordinated by a Claude Opus 4.5 agent, connected via Model Context Protocol to Git repositories and built with tools including Cursor and Ludus, testing a Python payload tool’s ~80 modules and 70-plus evasion techniques across dedicated VMs. Sophos linked the activity to ransomware and data-theft operations but didn’t name the group. It also noted the lab’s own documentation claimed the evasion modules improved with refinement, but the test data didn’t support those claims — likely LLM hallucination in the attacker’s tooling.

Why it matters: The signal isn’t “AI writes malware” — it’s that the crew built an automated test rig against three named commercial EDRs, compressing the develop-test-refine loop that previously demanded a skilled operator. The hallucination caveat runs in the defender’s favor: the self-reported evasion rates were inflated, so the real capability may trail what the repo advertises. Two of the three targeted EDRs are widely deployed across cloud-hosted endpoint estates.

5. Cross-Lab Review: The Four Major AI Providers Measure Prompt Injection With Incompatible Metrics

Primary source: VentureBeat Security
Related coverage: VentureBeat

What happened: A VentureBeat comparison published June 1 found that Anthropic, OpenAI, Google, and Meta each released prompt-injection disclosures in 2026, but no two used the same metrics — different test conditions, attack types, and success-rate definitions, with no shared adversarial test suite. Anthropic reported browser-agent hijacking rates; other labs focused on indirect injection in tool-calling or document-summarization tasks. The review advised teams to treat each lab’s numbers on their own terms and to request methodology before deploying agents in sensitive workflows.

Why it matters: Enterprises evaluating AI agents have no common denominator — a “31% before safeguards engaged” figure from one lab and a tool-calling success rate from another aren’t comparable, so procurement can’t rank models on injection resistance the way it ranks a CVSS score. With agents now writing and testing offensive code (story 4) and propagating through package ecosystems (story 1), the measurement gap sits on a control teams increasingly depend on.

6. White House Executive Order Sets Voluntary Federal Review and Cyber-Capability Benchmarking for Frontier AI Models

Primary source: The White House
Reporting: Roll Call · Council on Foreign Relations

What happened: On June 2 the White House issued “Promoting Advanced Artificial Intelligence Innovation and Security.” It directs Treasury, the NSA, and CISA to design, within 60 days, a voluntary framework under which developers may submit a frontier model for federal evaluation; “covered frontier models” would be made available to the government for up to 30 days before public release. The order establishes a classified NSA-led benchmarking process for offensive cyber capabilities and a voluntary AI cybersecurity clearinghouse to coordinate vulnerability discovery and patching. It explicitly creates no mandatory licensing, preclearance, or permitting requirement.

Why it matters: The load-bearing elements for security teams are the clearinghouse and the cyber-capability benchmark, not a compliance mandate — this is the government building a pre-release read on what frontier models can do offensively, the same measurement the Sophos lab (story 4) and the prompt-injection gap (story 5) show the private sector can’t yet produce consistently. Framing it as “increased regulatory scrutiny” overstates an order that imposes no obligation today.

7. Cisco Restructures Vulnerability Disclosure Around the AI-Compressed Exploit Gap, Adds Runtime Live Protect

Primary source (reporting): Axios · Help Net Security
Vendor primary: Cisco — disclosure cadence · Cisco — Live Protect

What happened: At Cisco Live 2026, Cisco said it will move to scheduled twice-monthly security advisories on the first and third Wednesdays of each month starting in July, with seven days’ advance notice of which technologies each release covers and a stronger risk-based emphasis on flaws under active exploitation. Cisco cited Talos data showing the interval between disclosure and first observed exploitation is compressing as attackers adopt AI automation. It also introduced Live Protect, which applies runtime compensating controls to shield a device against exploitation of a newly disclosed flaw — no reboot or upgrade required — while a permanent patch is staged.

Why it matters: The disclosure-cadence change is the signal, not the product: a major infrastructure vendor is rebuilding how it releases advisories because the disclosure-to-exploitation window is shrinking — the same dynamic the PAN-OS (story 2) and cgroups (story 3) KEV timelines show this week. Predictable windows let teams pre-stage change windows, but they also concentrate patch load on dates adversaries can anticipate. Live Protect is a bet that runtime compensating controls, not patching speed alone, become the near-term answer.

🎯 Cloud Security Topic of the Week:

Security at the Speed of Engineering — Guardrails, Not Gates

Every story above shares a clock problem. The Miasma worm executed at install time, the PAN-OS and cgroups flaws hit KEV faster than monthly patch cycles, the Sophos lab compressed the malware develop-test loop, and Cisco is rebuilding disclosure cadence specifically because the disclosure-to-exploitation window is shrinking. The week’s news is, in aggregate, a story about offense moving faster than the controls built to catch it.

That is exactly the problem Nick Reva designs around at DoorDash. His answer isn’t a new product — it’s a placement decision: put small security teams inside the product teams, and make the security signal arrive where engineers already work, on the pull request, at the speed they ship. Shivani Doke makes the parallel case for governance: stop treating GRC as an annual document refresh and start embedding controls in the lifecycle, validated at runtime. Two halves of one argument about where a control has to live to survive a faster attacker. [Listen to the full episode →]

Featured Experts This Week 🎤

Nick Reva — Global Security Engineering lead, DoorDash (previously Snapchat, SpaceX)
Shivani Doke — GRC Engineer
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Shift far left: Nick Reva’s extension of “shift left” — embedding small (3–5 person) security tiger teams directly into the product teams building AI features, hardening each surface in its own context with guardrails rather than approval gates.
Forward-deployed security teams / pods: Small embedded security teams that own the highest-priority product areas, credited on stage to Jason Chan’s “Netflix model.” Distinct from a central AppSec-tooling team that lacks per-team, per-month context.
Promptfoo: An open-source prompt-injection testing framework Reva likened to Burp Suite for AI — packaged rules you tweak to test for prompt injection, model efficacy, and model ethics, run as an integration test on the pull request.
“Claude Kiddies”: Reva’s coinage (a play on “script kiddies”) for low-skill actors who use AI to generate bug-bounty reports — and then to argue back against triage teams.
Security Knowledge Graph: Reva’s runtime control-validation system built on the open-source Cartography framework — it aggregates cloud and endpoint telemetry into a node graph, checks whether a designed control is operating against live runtime data (via eBPF probes observing pod security and Docker exposure), and auto-routes drift to the owning team via Slack or Jira.
GRC engineering: Shivani Doke’s discipline — moving GRC from point-in-time PDF-policy refreshes to controls embedded in the development lifecycle and validated continuously (runtime monitoring, RASP, attack-surface and supply-chain scanning, compliance enforced as code).
Nth-party / transitive vendor risk: Third-party vendors carry their own vendors (fourth-, fifth-, Nth-party); a breach anywhere in the transitive dependency chain can expose your data. Current Nth-party monitoring tooling is immature.
Human-in-the-loop: Reva’s design pattern for autonomous offensive/defensive agents — humans gate the critical decision points; the main-line work is mostly automated.

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💡Our Insights from this Practitioner 🔍

1. Shift far left: guardrails, not gates

The reason the week’s news keeps beating monthly patch cycles is that the attacker’s loop now runs at engineering speed. Reva’s response is to move security to where the engineers already are. Beyond the familiar “shift left,” he frames it as “shift far left”: small security teams embedded inside the product teams building AI features.

“far left means you embed small teams, small tiger teams of security engineers into the development teams are working on the AI initiatives to harden the AI initiatives.” — Nick Reva

The mechanism is deliberately not a gate. At a three-sided marketplace — dashers, merchants, customers — each surface carries a different AI experience and so a different threat model, and the control has to fit the way each team ships:

“the shift far left is establishing guardrails not gates into your product development story.” — Nick Reva

Concretely, that means integrating Promptfoo into every repo where AI development happens, writing lint-style rules that look for prompt injections in the agent frameworks in use, and building middle-tier service layers that filter both the prompt and the response. The connection to this week’s news is direct: story 5’s finding that the four major labs can’t agree on how to measure prompt-injection resistance is the strategic version of the same problem Reva solves tactically — if you can’t buy a comparable injection metric, you test injection yourself, in your own pipeline, against your own tool-calling paths.

2. Make the security signal arrive on the pull request

Guardrails beat gates because engineering teams move at engineering speed and won’t stop to ask permission. Reva’s design puts the security check inline, where an engineer is already looking:

“if, for example, an engineer opens up a PR on the repo for one of the… agent frameworks and Promptfoo runs as an integration test and gives them direct feedback on the PR, they’re gonna respond to it like, no, no engineer wants to ship… an agent framework that has prompt injection.” — Nick Reva

“You have to meet the team at the speed of that the team is operating at.” — Nick Reva

He noted the open-source version of Promptfoo is powerful enough that buying the commercial tier hasn’t been necessary, though they make it fit their SDLC — and that the same framework does double duty for model-efficacy and model-ethics testing. This is the practitioner answer to Cisco’s story-7 bet: when the disclosure-to-exploitation window shrinks, the control has to be already running where the work happens, not staged for a future change window.

3. Scale scarce security pods with AI-assisted threat modeling

Headcount never matches engineering scale, so the move is to make a small number of forward-deployed engineers far more productive.

“I have 2,500 engineers and I have three of these security pods… I don’t have 10, I don’t have 20, I probably never will from a headcount perspective. So how do I make those three people like really good at their job?” — Nick Reva

The experiment is AI-assisted threat modeling and product-security review — not generic ChatGPT, but context-specific models wired into the GitHub repos under review. The maturity sequence Reva described: make the human pods better first, iterate the model, then expose it to product teams as self-service.

“the idea is to make the forward deployed security engineer more productive. And then later the idea is to take this and give it to the product teams as like a Chrome extension… this agentic security review framework… we’ll have a virtual security engineer that’ll give you feedback that’s like really calibrated.” — Nick Reva

An audience practitioner described the same shape from the other side: a “baseline automated prodsec” approach that democratizes a SAST/DAST/SCA/threat-model baseline so every feature gets immediate early feedback, with humans reserved for complex code reviews, complex threat models, and tabletop exercises, gated by risk-based acceptance criteria before production.

4. Security decisions are business decisions — fund and own them accordingly

The forward-deployed model only works if everyone agrees on who owns the call. Reva is blunt that the security team isn’t the decision-maker:

“We’re led to believe security decisions are a security team responsibility. They’re not, they’re a business decision ultimately… The security team is the fact finder about risk and we help them provide technical solutions to solve problems, right? But ultimately the business makes the decision on what we wanna do.” — Nick Reva

The pod model itself he credits to a lineage:

“Jason Chan invented this idea of forward deployed security teams… that were deployed into the product areas in small tiger teams… And I’ve adopted this at DoorDash.” — Nick Reva

The budgeting takeaway he offered: align the pod’s funding ask with whatever business line security reports into (DoorDash’s reports into Legal, which he said works fine), and treat AI risk as one novel, complex category of business risk the board must hear about — not a siloed security cost.

5. “Claude Kiddies”: AI collapses the attacker skill floor — so triage with AI

Asked what AI attack actually keeps him up, Reva’s answer wasn’t novel malware. It was volume and the disappearance of the skill bar for low-end offense:

“the hobbyist level people are getting involved… There used to be like a… level of technical proficiency that you have to have to do this kind of work. It’s gone… ’cause they’re vibe coding the bug bounty reports.” — Nick Reva

His coinage for them — a play on “script kiddies” — was the line of the night: they’re “Claude Kiddies.” An audience member noted curl recently closed its bug-bounty program over an influx of AI-generated reports. Reva’s response is symmetric, and maps onto the Sophos lab in story 4 (offense automated, defense automates back):

“if they’re using Claude to generate bug bounty reports, we have to use Claude to triage those reports.” — Nick Reva

The nuance worth keeping: he argued bug bounties absolutely should still exist, because the genuinely novel, high-complexity vulnerabilities still won’t be found by automated pentest tooling, and “there shouldn’t be a race to the bottom in every category.” Triage the AI noise with AI; reserve human researchers for the consequential, novel work.

6. Human-in-the-loop at the critical decision points, automation on the main line

The bug-bounty discussion ran into the harder question: an autonomous offensive agent has no “rules of engagement” the way a human pentester does — you can’t easily tell it “this is far enough.” Reva’s model, which he tied to the Anthropic framing, is to gate the decisions that matter and automate the rest:

“it’s human in the loop for the critical decision points. And then… maybe like the main line aspects of it are… mostly automated, right?” — Nick Reva

For teams deploying offensive or autonomous security agents, the design pattern is to place explicit human approval at the consequential points — scope expansion, exploitation, lateral movement — rather than trying to encode complete rules of engagement up front, and automate the routine work between those gates. It’s the same principle the story-6 executive order reaches for at national scale: build the measurement and the review checkpoint, don’t pretend full autonomy is safe.

7. GRC engineering: move off the annual PDF refresh and embed controls in the lifecycle

Shivani Doke’s half of the conversation reframes governance the same way Reva reframes appsec. The reputation problem, she argued, comes from GRC being a point-in-time, document discipline:

“that’s where the beef… against the GRC folks really comes because we mostly focus on having all these policies. There’s this annual policy refresh where we just make some edits in the Word document in the PDFs… But we have to move away from that towards… really embedding these security controls within our development life cycles.” — Shivani Doke

On ownership, she rejects a one-team answer:

“who owns AI risk really depends on the use case.” — Shivani Doke

A business unit that requests an AI-forward vendor owns that vendor’s risk (via third-party assessment, model cards, continuous scanning); an in-house fine-tuned or foundational model puts ownership on the IT or developer-experience team building it. The concrete GRC-engineering control she described: if policy says no AI-agent-authored code can be merged, implement an automated flag — a two-person control — that blocks that code in the pipeline rather than attesting it in a document. An audience practitioner added the audit-automation angle: run a “SOC 2 every morning at 10:00 AM” to check whether built controls are still intact, and tie breaks to business risk before they become audit risk.

7. Continuous validation across an Nth-party attack surface

The most cloud-relevant control of the night ties both speakers together. Reva offered his “Security Knowledge Graph” — built on the open-source Cartography framework — as GRC engineering in practice: aggregate cloud and endpoint telemetry into a node graph and check, at runtime, whether a designed control is actually operating.

“you can even take controls that you’ve designed and say, is this control operating based on real time runtime data? So you have like eBPF probes, like observing your pod security… you can actually do that at runtime to see if that control is implemented. And if it isn’t… then you can fire a Slack notification or a Jira ticket to that owning team.” — Nick Reva

Doke supplied the reason that runtime validation now has to reach down the supply chain — the vendor surface is transitive:

“when you onboard a third party vendor, you’re also onboarding that third party vendor’s other dependencies, other vendors. So basically that becomes a transitive dependency. So if something happens to my data that’s been hosted on the third party’s cloud and the third party’s cloud security provider has a breach, then my data has been breached.” — Shivani Doke

This is the conversation’s tightest link to the week’s lead story. The Miasma worm (story 1) is exactly an Nth-party compromise — a trusted vendor’s namespace, poisoned upstream, stealing Kubernetes service-account tokens at install time — and the cgroups escape (story 3) is exactly the pod-boundary failure Reva’s eBPF probes are watching for. Doke flagged that current Nth-party monitoring tooling is “not… state of the art” and can’t yet be fully relied on; the practical posture is to interrogate each vendor’s own AI dependencies and downstream sub-processors, and to validate the controls you depend on against live runtime data rather than a signed attestation.

Practical takeaways for cloud security leaders

A few things senior cloud security leaders can act on in the next 30–60 days:

Put the security signal on the pull request. Run prompt-injection testing (e.g., Promptfoo) as an integration test in every repo where AI development happens, so engineers get inline feedback rather than a gate they route around.
Test injection yourself — don’t buy a comparable metric. The labs don’t agree on how to measure it, so run adversarial tests against your own tool-calling and retrieval paths before deploying an agent in a sensitive workflow.
Validate controls at runtime, not on paper. Stand up control-validation against live telemetry (eBPF pod-security probes, cloud + endpoint graph) and auto-route drift to the owning team via Slack or Jira.
Triage AI bug-bounty noise with AI. Use automation to clear the low-skill, AI-generated report volume and reserve human researchers for novel, high-complexity findings.
Reach down the supply chain. Treat install-time scripts and transitive (Nth-party) dependencies as a credential-theft surface — monitor preinstall/postinstall execution and interrogate each vendor’s own AI dependencies and sub-processors.

🧠 Mental Model — The Vendor List Was the Inventory. The Inventory Is Now the Vendor List.

For 20 years, security ran on gates: a review step that work had to pass through before it shipped. The gate worked because the attacker’s loop was slower than the approval cycle — there was time to stop, inspect, and sign off.

That timing assumption is now inverted. The Miasma worm executed at install time, the KEV deadlines beat monthly patch cycles, the Sophos lab compressed the malware develop-test loop, and Cisco is rebuilding its disclosure cadence around a shrinking disclosure-to-exploitation window. When offense runs at engineering speed, a gate is just a place the attacker has already passed.

The control that survives is the one already running where the work happens — on the pull request, in the pipeline, against live runtime telemetry — with a human reserved only for the consequential decision. Guardrails, not gates. Embed the control in the lifecycle, validate it continuously, and put the human at the point of risk acceptance, not at the door.

Podcast Episode

Full Episode with Nick Reva and Shivani Doke — Complete transcript and audio for this week's featured conversation

Question for you? (Reply to this email)

🤔 Is your security signal arriving on the pull request, or still waiting at a gate the attacker already ran past?

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🚨 Every Employee Vibe-Coding an App Is Now a Vendor - Igor and Jasper on Rebuilding TPRM for It

Ashish Rajan — Wed, 27 May 2026 21:41:51 +0000

This week's Cloud Security Newsletter topic: Third-Party Risk in the AI Era — Why Your Vendor Inventory Is Already Wrong (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

The week, Europe codified digital sovereignty as procurement law the same week the Netherlands used its investment-screening authority to block a US company from buying the host of its national identity system. Two actively-exploited vulnerabilities (Drupal, Microsoft Defender) hit federal patch deadlines. A North Korean Lazarus subgroup went fully memory-resident, neutralizing most filesystem-based forensics in financial-sector intrusions. And Anthropic shipped a free in-IDE security review plugin for Claude Code that moves AppSec scrutiny inside the AI coding loop developers are already using.

This week's conversation is with Igor Andriushchenko, CISO at Lovable (and 4x prior CISO across telco, AI, and medical-device companies), and Jasper Mills, co-founder and CEO of ethira, hosted by Ashish Rajan. The thread running through both the news and the episode: trust assumptions are breaking faster than the programs built to manage them. The vendor list is wrong because half the new vendors are five-person AI companies. The "vendor" category itself is wrong because every employee building with AI is functionally introducing third parties without procurement ever seeing them.[Listen to the episode]

⚡ TL;DR for Busy Readers

- Netherlands blocks Kyndryl–Solvinity acquisition (DigiD host). EU Tech Sovereignty Package follows next day. CLOUD Act is now a procurement gate, not a legal-theory debate.

- Drupal CVE-2026-9082 actively exploited. CISA federal deadline today. PostgreSQL-only, CVSS 6.5 understates the operational risk.

- Microsoft pushed out-of-band patches for two Defender zero-days (RedSun, UnDefend) after six weeks of LPE exploitation. The EDR is the escalation path.

- Lazarus deployed memory-only RemotePE against financial and crypto firms. Filesystem-based EDR triage fails. Memory acquisition belongs in your IR runbook now.

- Igor + Jasper's frame: "second-party risk" — every employee vibe-coding an app with an MCP attached is a vendor your TPRM program does not know about.

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. Netherlands Blocks Kyndryl–Solvinity Acquisition; EU Tech Sovereignty Package Lands the Next Day

Primary source: European Commission Digital Strategy
Reporting: TechCrunch · DutchNews
Analysis: CNBC · TheNextWeb

What happened: On May 26, Dutch State Secretary for the Digital Economy Willemijn Aerdts issued a "complete prohibition" on Kyndryl's acquisition of Solvinity — the Dutch cloud provider that hosts DigiD, the national digital identity system used by millions of citizens to access tax, health, and government services. It is the first acquisition the Dutch Investment Screening Bureau (BTI) has ever fully blocked. The Dutch competition authority cleared the deal on antitrust grounds in February; the separate investment-screening review reached the opposite conclusion on public-interest grounds. The named concern was the US CLOUD Act.

One day later, the European Commission unveiled its long-delayed Tech Sovereignty Package, which includes the Cloud and AI Development Act (CADA) and Chips Act 2.0. The package proposes to restrict EU member-state governments from using US-headquartered cloud platforms for sensitive public-sector data in healthcare, finance, and judicial systems. CLOUD Act is again the named cause. The package still requires all 27 member-state approvals.

Why it matters: This is the first time the CLOUD Act has been codified as a procurement-disqualifying condition at EU scale rather than litigated through Schrems II–style data-protection rulings. The threat model is no longer hypothetical legal risk — it is a procurement gate. For US-headquartered enterprises with EU operations, the assumption that AWS, Azure, and GCP regions in Frankfurt, Dublin, or Paris are functionally interchangeable with sovereign-EU alternatives for sensitive public-sector contracts is now wrong. For European enterprises in regulated sectors, sovereignty review is moving from a contracts question to an architecture question. This connects to Jasper's point in this week's conversation: contractual accountability under DORA is the closest existing analogue, and the same mechanism — written guardrails that follow the data — is what regulators are now extending across the rest of EU procurement.

2. Drupal CVE-2026-9082 — Actively Exploited, CISA Federal Deadline Today

Primary source: Drupal advisory SA-CORE-2026-004
Reporting: The Hacker News · BleepingComputer
Analysis: CISA KEV Catalog

What happened: Drupal disclosed CVE-2026-9082 on May 19 — an unauthenticated SQL injection in Drupal Core's database abstraction API affecting PostgreSQL-backed deployments. Drupal rated it "highly critical" (23 of 25 on its internal severity scale). Discovered by Google/Mandiant researcher Michael Maturi. Within 48 hours of patch release, Drupal updated its advisory to confirm exploitation in the wild. CISA added the CVE to KEV on May 22 with a federal civilian remediation deadline of May 27 under BOD 22-01. Imperva reported observing over 15,000 attack attempts against nearly 6,000 sites across 65 countries, with gaming and financial services sites comprising roughly half the attack traffic. MySQL, MariaDB, and SQLite-backed deployments are not affected.

Why it matters: Drupal Core sits behind a long tail of government, education, research, and enterprise public-facing sites. The vulnerability is unauthenticated and the gap between disclosure and in-the-wild exploitation was under 48 hours. Programs that triage by CVSS alone will deprioritize this — the score is 6.5, lower than the operational risk. EPSS and KEV are the better signals. The PostgreSQL specificity is a natural triage gate, but only if asset inventory is current enough to answer "which Drupal sites are on Postgres?" without paging someone.

3. Microsoft Defender Zero-Days RedSun and UnDefend — Out-of-Band Patches After Six Weeks of Exploitation

Primary source: Microsoft Security Response Center
Reporting: BleepingComputer · SecurityWeek
Analysis: TechTimes (Huntress confirmation)

What happened: On May 21, Microsoft pushed out-of-band patches for two Windows Defender zero-days — CVE-2026-41091 "RedSun" (CVSS 7.8) and CVE-2026-45498 "UnDefend" (CVSS 4.0) — after six weeks of confirmed in-the-wild exploitation. RedSun is a local privilege escalation in the Microsoft Malware Protection Engine ≤1.1.26030.3008 caused by improper link resolution before file access. A low-privileged user can manipulate a symbolic link or directory junction during a Defender scan to escalate to SYSTEM. UnDefend is a DoS flaw exploited by standard users to block Defender definition updates. Both were originally disclosed publicly without coordination by a researcher operating under the aliases "Chaotic Eclipse" / "Nightmare Eclipse" between April 3 and April 16. The first in the series (BlueHammer, CVE-2026-33825) was patched April 14. RedSun and UnDefend went unpatched for six weeks while Huntress confirmed exploitation in hands-on intrusions. The same engine update (Microsoft Defender Antimalware Platform 4.18.26040.7) also fixes CVE-2026-45584, a heap-based RCE (CVSS 8.1) not yet confirmed exploited. CISA added RedSun and UnDefend to KEV on May 20 with a federal deadline of June 3.

Why it matters: The exploited flaws are in the endpoint agent itself. The defensive control becomes the privilege-escalation vector — compromised low-privilege accounts reach SYSTEM through the AV the SOC trusts. This inverts the trust direction of the control, which makes it the most consequential class of EDR/EPP bug. For Windows cloud workloads (VDI, RDS gateways, jump boxes, Citrix farms on Azure, AWS, or GCP), this is the lateral-movement layer. CVE-2026-45584 — RCE without user interaction — is the one to watch. Exploitation isn't confirmed yet, but the technical bar is the lowest in the bundle.

Primary source: Microsoft Security Response Center advisory
Reporting: The Hacker News · Help Net Security
Analysis: Dark Reading

What happened: Microsoft released patches for CVE-2026-45659 (CVSS 8.8), a high-severity RCE in on-premises SharePoint disclosed as part of May 2026 Patch Tuesday (advisory published May 21, broader coverage May 26–27). The flaw is a deserialization-of-untrusted-data issue (CWE-502). An attacker with only Site Member permissions — no admin rights, no elevated privileges — can execute code remotely on a SharePoint Server instance. Network vector, low complexity, no user interaction. Affected: SharePoint Server Subscription Edition, SharePoint Server 2019, and SharePoint Enterprise Server 2016. Microsoft assesses exploitation as "less likely" with no public PoC at disclosure, but the 2025–26 pattern for SharePoint deserialization bugs has consistently been reclassification upward within weeks once PoCs surface — CVE-2026-32201 followed that pattern and was added to CISA KEV in April.

Why it matters: "Authenticated but only Site Member" is almost no bar at all. SharePoint Site Member permissions are routinely granted to contractors, vendors, business-line partners, and broad employee groups. Treat this as one credential-compromise hop from RCE. The conservative move is to patch on Microsoft's original cadence, not the assessed-likelihood cadence. SharePoint Online is patched centrally by Microsoft; the hybrid and on-prem footprint is where the residual risk concentrates.

5. Lazarus Group Deploys RemotePE — Fully Memory-Resident RAT Against Financial and Crypto Firms

Primary source: Fox-IT (NCC Group)
Reporting: SC Media · The Hacker News
Analysis: Cryptopolitan

What happened: On May 22, NCC Group subsidiary Fox-IT published research on a new toolset deployed by a North Korea-linked Lazarus subgroup (overlapping with AppleJeus, Citrine Sleet, UNC4736, and Gleaming Pisces) in IR engagements against financial and crypto organizations. The toolset has three components forming a chain: DPAPILoader decrypts and loads RemotePELoader from disk using the Windows Data Protection API; RemotePELoader beacons to a C2 server and receives RemotePE, a RAT executed entirely in memory with no filesystem artifacts. The chain uses environmental keying via DPAPI (the second-stage loader can only be decrypted on the originally-infected host), Hell's Gate direct syscalls, and ETW patching. Initial access is via Telegram social engineering, with the actor impersonating trading-firm employees using cloned Calendly and Picktime scheduling pages. This toolset replaced the actor's previous ThemeForestRAT and PondRAT.

Why it matters: Memory-only execution plus DPAPI environmental keying defeats most filesystem-based forensics. The standard EDR triage workflow — pull artifacts, find the dropped binary, hash and pivot — collapses. The DPAPI keying is the technically interesting part: even if defenders capture RemotePELoader, they cannot decrypt the next stage on a different machine. That is anti-collaboration design by construction. The targeting (trading firms, DeFi, banks with international operations) is the same population that runs the most sensitive cloud workloads. AWS, Azure, and GCP credentials, BI tool API keys, and trading-platform integrations are the actual prize.

6. Iranian APT Nimbus Manticore — AI-Assisted MiniFast Backdoor, AppDomain Hijacking, SEO Poisoning

Primary source: Check Point Research
Reporting: The Hacker News · SecurityWeek
Analysis: Infosecurity Magazine

What happened: On May 22, Check Point Research published "Fast and Furious — Nimbus Manticore Operations During the Iranian Conflict," documenting three waves of activity between February and April 2026 by the IRGC-affiliated threat actor Nimbus Manticore (also tracked as UNC1549, Screening Serpens). The campaigns coincide with Operation Epic Fury, the joint US–Israeli military operation that began February 28. Targets: aviation, software, defense, and telecommunications organizations across the US, Europe, Saudi Arabia, and Australia. Three tradecraft shifts: AppDomain hijacking replaces DLL sideloading (a trojanized XML .config file placed next to a legitimate .NET application loads an attacker-controlled DLL via the AppDomainManager class); a new backdoor named MiniFast replaces the older MiniJunk family, with hallmarks Check Point attributes to AI-assisted development; and SEO poisoning via a counterfeit Oracle SQL Developer download page. The March wave used a trojanized Zoom installer.

Why it matters: AppDomain hijacking is well-known but underweighted relative to DLL sideloading in most hunt programs. The detection signal — an XML .config file appearing next to a legitimate .NET binary, named after the abused binary with a .config suffix — is concrete and hunt-friendly. AI-assisted malware development is no longer a forecast. Check Point's fingerprints (excessive error handling on trivial functions, verbose repetitive naming, debug-style status strings) are now indicators. State-actor capability ramp times are getting shorter. Aviation, defense, and software-supplier organizations in Australia and Saudi Arabia in scope is worth flagging for AU/NZ readers — Iranian APT activity is not historically the top concern for that region.

7. Anthropic Ships Claude Code Security-Guidance Plugin and Self-Hosted Sandbox

Primary source: Anthropic announcement (via SecurityWeek)
Reporting: Help Net Security · Cybersecurity News
Analysis: Anthropic open-source reference (GitHub)

What happened: At the Code w/ Claude event in London the week of May 26, Anthropic announced two security-relevant features for Claude Code, its terminal-based AI coding agent. The security-guidance plugin (free, all plans, launched May 26) is a three-layer reviewer that runs inside the Claude Code session. Layer one is a deterministic regex pass with no model call — it catches around 25 dangerous patterns (eval, os.system, child_process.exec, pickle deserialization, dangerouslySetInnerHTML and similar) at zero usage cost. Layers two and three are deeper agentic reviews triggered on model turns and on commits, reading surrounding callers and sanitizers to minimize false positives. Anthropic's internal rollout reported a 30–40% reduction in security-related PR comments. Anthropic separately announced a public-beta self-hosted sandbox: Claude Managed Agents now run tool execution in customer-controlled environments (the customer's own infrastructure or a managed provider like Cloudflare, Daytona, Modal, or Vercel), while the orchestration loop stays on Anthropic infrastructure. Files, repositories, and runtime images stay inside the customer perimeter.

Why it matters: AppSec embedded in the AI coding loop is the architectural shift the senior reader's developers are already living through. Free, zero-config security review at the point of code creation collapses the developer-friction tax that has been the longstanding gap in shift-left. The self-hosted sandbox addresses the most concrete enterprise objection to agentic AI coding — that agent-executed code, files, and secrets leave the corporate perimeter. Cloud security architects now have an architecture pattern to point to: agent reasoning external, execution internal. This connects directly to Igor's framing in this week's conversation. When employees build with AI agents and connect MCPs to internal data, the company is creating second-party risk on a continuous basis. Tooling that catches issues at the point of creation is the only realistic way to keep up.

🤖 8. Akamai to Acquire LayerX for $205M — Browser-Layer AI Usage Control Becomes a Platform Feature

Announced May 14 — outside the strict 5-day window but included as the largest cybersecurity M&A of May 2026 and directly relevant to the AI-governance thread running through this week's news.

Primary source: Akamai press release
Reporting: SecurityWeek · Help Net Security
Analysis: BankInfoSecurity

What happened: Akamai announced a definitive agreement to acquire LayerX, a Tel Aviv–based browser-native security firm, for approximately $205 million in cash. LayerX provides AI usage control and secure-enterprise-browser (SEB) technology that runs on top of standard browsers (Chrome, Edge, Safari) rather than requiring users to switch to a proprietary browser. The platform covers shadow AI discovery, gen-AI data loss prevention, access controls for AI tools, and protection for agentic browsers (Atlas, Comet). The deal is expected to close in Q3 2026. This is Akamai's third Israel-based security acquisition after Guardicore (2021, ~$600M) and Noname Security (2024, ~$450M).

Why it matters: Browser-layer telemetry is becoming a category. The deal validates that "AI usage control" — which workforce uses which models with which data — is a platform requirement, not a standalone product. For cloud security architects, this signals a likely shift in vendor roadmaps: browser-based DLP, SaaS access governance, and AI-tool inventory will converge with ZTNA and CASB rather than sit alongside them. Expect Zscaler, Palo Alto, and Netskope to respond with comparable consolidation moves. The agentic-browser detail (Atlas, Comet) is the forward-looking part. If workforce starts using AI browsers that act on their behalf, the security control point has to live there — network-layer DLP cannot see what a browser-resident agent does inside a session.

🎯 Cloud Security Topic of the Week:

Third-Party Risk in the AI Era - Why Your Vendor Inventory Is Already Wrong

The pre-AI state of third-party risk management was, in Igor's word, "abysmal." A spreadsheet of 200-question vendor checklists, hours filled out by both sides, generating documentation nobody reads until an auditor asks for it. Both sides know it is a paper exercise. Both sides do it anyway.

What Igor and Jasper lay out in this week's conversation is that the program was barely surviving the old model when AI broke three assumptions underneath it at once. The vendor list shrank from a handful of large suppliers to dozens of five-person AI companies whose risk profile a 200-question checklist cannot meaningfully assess. The "vendor" boundary itself stopped holding — when an employee builds an internal app with AI and wires an MCP server into Salesforce, no procurement event has occurred, but a third party has effectively been introduced inside the perimeter. And the pace at which both sides operate started moving toward agent speed. Pactum is already running agent-to-agent procurement negotiations. The same architecture applied to vendor questionnaires is technically obvious. The only thing holding it back is comfort.

That is the framing for this week's conversation. Igor and Jasper are not predicting a distant future. They describe a transition already happening in pieces — and a series of practical decisions cloud security leaders need to make in the next 12–18 months about what to automate, where to keep humans in the loop, and how to inventory a class of "vendors" the existing TPRM program cannot see. [Listen to the full episode →]

Featured Experts This Week 🎤

Igor Andriushchenko — Head of Information Security & CISO, Lovable
Jasper Mills — Co-founder & CEO, ethira
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Third-Party Risk Management (TPRM): Assessing, monitoring, and governing the security and compliance posture of external suppliers — historically driven by vendor questionnaires (SIG, CAIQ), SOC 2 reports, pen test attestations, and contractual clauses.
Second-Party Risk: Igor's term for the risk introduced when internal employees or departments build their own applications with AI — functionally creating new "vendors" the procurement-driven TPRM program never sees.
MCP (Model Context Protocol): A protocol that lets AI agents connect to external tools and data sources through standardized connectors. An MCP server bridges a language model to systems like Salesforce, Git, databases, or internal APIs. From a TPRM perspective, every MCP connection is a privileged integration point.
Shadow AI: AI tools adopted by employees without security or procurement review. A common pattern: employees bypass enterprise restrictions on a sanctioned tool by creating a personal account and re-enabling the feature there.
DORA (Digital Operational Resilience Act): EU financial-sector regulation that requires ongoing, contractual accountability for ICT third-party providers — including subcontractors, exit strategies, and continuous monitoring. The closest existing analogue for how regulators will likely govern AI agents.
Agent-to-Agent (A2A): The emerging pattern where one organization's AI agent communicates directly with another organization's AI agent — for procurement negotiation, vendor onboarding, or security questionnaire exchange. Pactum is one of the early production examples.
CLOUD Act: US law (2018) that lets American law enforcement compel US-headquartered cloud and technology providers to disclose customer data regardless of where the data is physically stored. The named concern behind both the Dutch Kyndryl–Solvinity block and the EU Tech Sovereignty Package this week.

This week's issue is sponsored by Tamnoon

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💡Our Insights from this Practitioner 🔍

1. The pre-AI state was a paper exercise. AI doesn't fix it — it raises the stakes.

The opening framing from Igor lands the first point hard:

"If you take a security program, right? There is a whole bunch of security program dedicated, any security program dedicated to third-party risk management. You can fail a lot of audits on it. It requires a lot of documentation, a lot of rigor. And the more, the bigger company becomes, the more impossible it becomes to control your vendors the way you actually bring value. It becomes this kind of paper exercise where you do something for the sake of doing it." — Igor Andriushchenko

Jasper's experience implementing DORA at her previous company hit the same wall from a different direction — DORA's accountability requirements collided with the AI-tooling wave at exactly the moment her organization was trying to onboard productivity tools at speed:

"The process of, like, using the tools on the market felt like I'd been catapulted to 1979. It was the worst experience of my professional career." — Jasper Mills

The practical implication for senior cloud security leaders: the program does not need optimization. The cadence at which AI-driven vendors arrive, and the rate at which employees create new internal applications that behave like vendors, is fundamentally incompatible with a checklist-driven model. Optimization within the existing frame produces the same paper exercise faster.

2. Second-party risk is the new category — and the existing program cannot see it

The most useful new vocabulary in the conversation is Igor's distinction between third-party and second-party risk:

"Should we treat each of these people or their departments as mini vendors? Because essentially we need to apply to those, whatever they produce, some kind of rules, some kind of governance. And that governance is very similar in its nature to third party risk. You're, like, it's almost like it's a second party risk. It's your employees, it's your builders." — Igor Andriushchenko

Jasper's working example crystallizes the operational problem:

"Or maybe John built it themselves... and then he found an MCP that he has spun up and now it's going to Salesforce and, like, fetching whatever it wants." — Jasper Mills

There is no procurement event for John. No SOC 2 review. No vendor questionnaire. There is an MCP server reaching into a CRM, configured by someone in a non-technical team, governed by nothing. From a TPRM program's perspective, John's app does not exist. From an actual risk perspective, it is one of the highest-velocity new sources of exposure in the organization.

The practitioner translation: TPRM coverage has to extend to inventory the program does not currently own. Discovery of internal apps, MCP servers, and the data sources they touch needs to live alongside the vendor inventory — and the same risk-tiering language needs to apply to both.

3. The "kindergarten with a nuclear bomb" problem

Jasper's most-quoted line in the episode comes from a customer conversation, and it captures why the AI-productivity push is currently outrunning the controls:

"A CISO called me and he said, like, 'We have just given a kindergarten a nuclear bomb.'" — Jasper Mills (recounting a customer conversation)

The CISO had enabled AI tooling for sales and other non-technical teams. The technology worked. The productivity gains were real. Visibility into what those teams were doing with the tooling — where data was going, which integrations had been wired up — was effectively zero. The control plane lagged the adoption plane by months.

Igor frames the dynamic from the other side — what's happening inside the user's head when they make the choice to bypass a sanctioned tool:

"People just create a personal workspace, and they just enable that feature. Everything feels solvable, just, like, one toggle away." — Igor Andriushchenko

When the friction between "thing I want to do" and "thing I can do" collapses, the security implication is no longer a calculation most users perform. The ergonomic gap between sanctioned and unsanctioned has to close, or the workforce will close it for the program.

4. What actually gets automated — and what stays human

Both speakers are practical about where the line sits today. Jasper described what ethira automates and what it explicitly does not:

"We can do all of that autonomously. Where we actually pull the humans in is at the end. So we basically aggregate everything that we cannot get, or we'll give an analysis based on your risk tolerance — this is sort of what we would recommend, and these are the mitigating factors if you want to onboard or not." — Jasper Mills

The automation absorbs data gathering, financial and news checks, GitHub-based open-source maintenance signal, pen test request workflows, and the back-and-forth that historically dominated TPRM analyst time. Human judgment stays at the point of risk acceptance, where it belongs.

Igor reinforces the same pattern from the consumer side and recommends an underrated starting point that does not require new procurement:

"I'm very excited about AI doing inventory of everything that's going on in the company, 'cause we already have some solutions. They just look into telemetry from the device. Let's say they take CrowdStrike telemetry, they take any other agent kind of running on your computer telemetry, and then they analyze it, and it was like, 'Oh, I found these 75 vendors here.'" — Igor Andriushchenko

Practitioner takeaway: AI-driven inventory is the highest-leverage place to start. Most enterprises already have the telemetry sitting in their EDR. What they don't have is a vendor list reconciled against it. Running AI over existing endpoint telemetry to produce a continuous vendor inventory closes a gap the spreadsheet has never been able to close.

5. Build vs. buy for the TPRM stack — both speakers come down on "buy"

This is one of the rare sections where two AI-native operators arrive at the same conclusion from different angles. Igor's reasoning is operational, not philosophical:

"Imagine you have to build your third party risk management from scratch... how many people are working on that really? Like, is there one engineer who's vibe coding it? Good. But then what happens next? It needs to be maintained. Somebody needs to take a look at logs, at alerts, at telemetry... and you end up with somebody whose full-time job is just maintaining that app." — Igor Andriushchenko

He extends the point to audit posture: if it was your own fault, you decided to take that risk and build it yourself, then it could be a more serious issue.

Jasper makes the same point from the vendor side — the unique data sources and hallucination-mitigation work that a serious TPRM product invests in are not realistic to replicate as a side project. For cloud security leaders evaluating whether to build internal TPRM tooling on top of foundation-model APIs, the maintenance tail and the audit-defensibility tail are typically larger than the build cost, and neither shows up in the initial estimate.

6. Where this ends up: agent-to-agent procurement, contractual accountability as guardrails

Both speakers point at the same destination. Igor names the year:

"I've heard it many times, 2027 is the year of agent to agent. We are looking at third party management agent, risk management agents talking to company agents, like vendor agents that are just there listening for anyone coming in, asking about pen test results or NDA or something like that... It may sound bad, but there is no place for humans in that loop." — Igor Andriushchenko

Jasper draws the architectural line back to the regulatory anchor that the whole conversation circles:

"DORA, one of the key sort of foundations is contractual accountability. And one of the things that we've thought about is if you think about people, if you think about agents, if you think about vendors, historically what you've been able to do, if you look at when something goes wrong, you go back to the contract, you call a person up. But actually what you're able to do with vendors, with third-party agents, even with first-party agents, you can then actually take the contract that you have and create guardrails." — Jasper Mills

The contract becomes the guardrail. The guardrail becomes the policy the agent operates under. The audit trail becomes the proof that the policy was followed. That sequence — contract → guardrail → enforceable policy on agent behavior — is the most actionable architectural insight in the episode for senior cloud security leaders thinking about how their TPRM program survives 2027.

7. The "IT becomes HR" frame — managing agents like contracted workforce

Jasper's closing prediction is the one to sit with:

"IT will end up being like HR, in the fact that it will be like your contracted workforce. So you'll have a lot of contracted agents, you'll have a life cycle, you'll have a cost within that. They have their own credentials. But I think you'll manage it very similarly to third party risk, and the vendors will eventually have their own agents that are working in your systems." — Jasper Mills

Igor extends the metaphor into something more uncomfortable — and useful for designing controls:

"What defines a person is the agency. We have free will. We decide what to do next. The agents have that too. Not to the same extent, of course. There is a program where there is intention we give them, but still, sometimes they do things we do not expect." — Igor Andriushchenko

"The moment we start thinking, 'Hey, this thing will behave deterministically,' we've failed as security people." — Igor Andriushchenko

The practical implication: the controls that work for non-deterministic actors (humans, contractors, agents) are different from the controls that work for deterministic systems. Audit logging at every action, source-and-destination metadata, scoped credentials, lifecycle management with onboarding and offboarding, and behavioral monitoring against a baseline — these are HR-adjacent controls. They are not the controls most TPRM programs are set up to run.

Practical takeaways for cloud security leaders

A few things senior cloud security leaders can act on in the next 30–60 days:

Start inventory with the telemetry you already have. Run AI over existing EDR telemetry to build a continuous vendor and application inventory before paying for a new tool. Most enterprises have the data — they just don't have the reconciled list.
Add a "second-party" track to TPRM. Internal apps built with AI and connected to internal data sources (especially via MCP) need a risk-tiering process equivalent to the one used for external vendors. Procurement isn't going to catch these.
Treat MCP servers as privileged integrations. Every MCP connection from an internal app to a SaaS data source (Salesforce, Git, internal APIs, CRM) is a privileged integration. Inventory, scope, and audit them with the same rigor applied to service accounts.
Pick the autonomy level deliberately. Match the toggle to the risk class. Agent-to-vendor questionnaire negotiation is reversible and low-blast-radius; agent-driven risk acceptance is not.
Map contractual accountability to agent policy now. Whether the organization is regulated under DORA or not, the contract → guardrail → policy sequence is the architectural pattern that survives the agent-to-agent transition. The teams that have written this out before 2027 will not have to retrofit it under regulatory pressure.

🧠 Mental Model — The Vendor List Was the Inventory. The Inventory Is Now the Vendor List.

For 20 years, TPRM ran on a procurement-driven vendor list. Procurement onboarded a supplier, security reviewed it, the supplier went on the list, the list got audited. The list was the inventory.

That sequence is now backwards. The inventory — what your EDR sees running, what your network sees connecting, what your developers have wired up — is the source of truth. The vendor list is a downstream projection of it, and an increasingly incomplete one. Procurement no longer sees a meaningful share of the third parties operating inside the perimeter, because employees are creating them with AI faster than procurement can intake them.

The program that survives 2027 starts from inventory and projects out to a vendor list. Not the other way around.

ethira — Jasper's company; product focus on automated TPRM and agent governance
Lovable — Igor's company; AI-native app builder
EU Digital Operational Resilience Act (DORA) — Official text and guidance for financial-sector ICT third-party accountability
Anthropic Model Context Protocol (MCP) — Protocol documentation and best-practice guidance for securing AI agent connectors
CISA Known Exploited Vulnerabilities (KEV) Catalog — For Drupal, Microsoft Defender, and SharePoint vulnerability tracking referenced this week
Fox-IT — RemotePE technical research — Lazarus toolset analysis and detection guidance
Check Point Research — Nimbus Manticore — IRGC-affiliated APT activity, AppDomain hijacking, and AI-assisted malware fingerprints
OWASP Top 10 for Agentic Applications — For organizations extending AppSec frameworks into AI-assisted development

Podcast Episode

Full Episode with Igor Andriushchenko and Jasper Mills — Complete transcript and audio for this week's featured conversation

Question for you? (Reply to this email)

🤔 Is your TPRM program seeing the apps your own employees built with AI this quarter or just the vendors procurement onboarded?

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🚨 GitHub Breach Caps TeamPCP's 5-Compromise Run - Sergej Epp on Why Defense Has No Verifiers

Ashish Rajan — Wed, 20 May 2026 20:52:22 +0000

This week's Cloud Security Newsletter topic: The Cybersecurity Verification Law — Why Offence Has a Superpower and What Defence Can Do About It (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

This week's news has one through-line: trust is breaking down in the developer tooling layer. TeamPCP reached 3,800 GitHub internal repositories through a single poisoned VS Code extension on one employee's laptop. The Mini Shai-Hulud worm compromised TanStack's legitimate release pipeline and dragged OpenAI, Mistral, UiPath, and Guardrails AI into the blast radius. Grafana's GitHub token was lifted and its codebase extorted. Microsoft Exchange OWA has been under active exploitation for six days with no patch. And Verizon's 2026 DBIR confirmed it at industry scale: vulnerability exploitation has overtaken stolen credentials as the #1 initial access vector for the first time in 19 years.

This week's conversation is with Sergej Epp, CISO at Sysdig and former CISO at Deutsche Bank and Palo Alto Networks, hosted by Ashish Rajan. Sergej's framing pulls the news together: offense has cheap binary verifiers — pop a shell, capture the flag, exfiltrate the secret. Defense doesn't. Until that gap closes, the speed asymmetry only widens.[Listen to the episode]

⚡ TL;DR for Busy Readers

- GitHub confirmed ~3,800 internal repos stolen via poisoned VS Code extension. TeamPCP's 5th 2026 supply chain hit. Audit dev extensions now.

- Exchange OWA zero-day (CVE-2026-42897) active 6+ days, no patch. EEMS mitigation doesn't cover IE-mode users. Run ExchangeHealthChecker.

- Verizon DBIR 2026: vuln exploitation overtakes credential theft for first time in 19 years. 22,000 confirmed breaches, 60% YoY rise in third-party involvement.

- Mini Shai-Hulud worm hit 170+ packages May 11 — caught two OpenAI 
  employee devices. First malicious npm package with valid SLSA provenance.

- Sergej Epp's argument: offense has cheap binary verifiers (shell popped or not), defense doesn't. The program that wins takes humans out of the response loop before attackers do.

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. GitHub Confirms ~3,800 Internal Repositories Stolen via Poisoned VS Code Extension

Primary source: GitHub statement on X (May 20)
Reporting: SecurityWeek · BleepingComputer
Analysis: Help Net Security · Infosecurity Magazine

What happened: GitHub confirmed on May 20 that approximately 3,800 internal repositories were exfiltrated after a single employee installed a malicious Visual Studio Code extension. "Our current assessment is that the activity involved exfiltration of GitHub-internal repositories only. The attacker's current claims of ~3,800 repositories are directionally consistent with our investigation so far," GitHub stated. The TeamPCP group claimed responsibility on the Breached cybercrime forum, listing the data for $50,000 with a threat to leak it free if no buyer surfaces. GitHub has stated there is no evidence of customer repository impact, but the investigation is ongoing. This is TeamPCP's fifth major supply chain compromise of 2026. The group has previously compromised Aqua's Trivy security scanner, CheckMarx's KICS, the LiteLLM library, the Telnyx SDK, TanStack, MistralAI, and other packages that depended on those.

Why it matters: A single VS Code extension on one employee's machine reached 3,800 internal GitHub repositories. "Developer workstations are the number one target in supply chain attacks right now, and this is exactly why," Aikido Security's Mackenzie Jackson said. "Most security teams still have zero visibility into what extensions or packages are on their developers' machines, or how recently they were published." For cloud security architects, this collapses three trust boundaries at once: the IDE marketplace as a software distribution channel, the developer workstation as a privileged access endpoint, and the source-code repository as a sensitive data store. GitHub is the platform 90% of the Fortune 100 builds on; the breach hit its own internal code, meaning downstream organizations now face a 6–12 month window where attackers may probe leaked source for novel exploits.

Action for defenders: Build a real inventory of IDE extensions across your engineering org — VS Code, JetBrains, Cursor, Windsurf. Restrict installation to an allowlist of verified publishers. Treat developer workstations as Tier-0 assets with EDR coverage matching domain controllers. Move CI/CD authentication to short-lived OIDC tokens. Rotate any GitHub PATs, npm tokens, or cloud credentials a developer may have touched in the past 30 days.

2. Microsoft Exchange OWA Zero-Day (CVE-2026-42897) Under Active Exploitation - No Patch Six Days In

Primary source: Microsoft Security Response Center advisory
Reporting: SecurityWeek · BleepingComputer
Analysis: Dark Reading

What happened: Microsoft confirmed on May 14 that CVE-2026-42897 — a cross-site scripting flaw in the Outlook Web Access component of Exchange Server 2016, 2019, and Subscription Edition — is under active exploitation in the wild. An attacker needs only to send a crafted email; if the recipient opens it in OWA, arbitrary JavaScript executes inside their authenticated browser session, enabling session token theft, mailbox impersonation, and email rule manipulation without the attacker ever touching the server itself. No permanent patch exists.

CISA added the vulnerability to its Known Exploited Vulnerabilities catalog on May 15, giving Federal Civilian Executive Branch agencies until May 29 to apply mitigations. A May 18 update added that the mitigation does not protect users accessing OWA through Internet Explorer or Microsoft Edge in Internet Explorer compatibility mode. Exchange Online is not affected.

Why it matters: Six days into active exploitation with no permanent patch, mitigations that disable features, and an explicit carveout for IE-compatibility-mode users. On-prem Exchange remains one of the most consistently exploited entry points to enterprise environments, and an XSS-class flaw in OWA can convert directly to session-token theft and mailbox impersonation — and from there, into lateral movement into cloud identity systems via Entra Connect.

Action for defenders: Verify EEMS is enabled and mitigation M2.1.x has been applied automatically. Run aka.ms/ExchangeHealthChecker. For air-gapped environments, manually apply EOMT. Identify any user populations still using IE or Edge IE-mode for OWA and force them off until a patch ships. Treat any anomalous mailbox rule creation in your OWA logs from May 14 onward as a high-priority IR signal.

🛠 If you only do one thing this week: Audit IDE extensions across your engineering org and rotate every GitHub PAT and cloud credential a developer has touched in the last 30 days. The same payload pattern that hit GitHub itself is the one that caught TanStack, Mistral, and two OpenAI employee devices nine days earlier.

3. Verizon 2026 DBIR: Vulnerability Exploitation Overtakes Credential Theft for the First Time in 19 Years

Primary source: Verizon DBIR 2026 announcement
Reporting: SecurityWeek · Dark Reading
Analysis: Qualys research partner breakdown

What happened: Verizon published its 19th annual Data Breach Investigations Report on May 19. More than 22,000 confirmed breaches, nearly double last year's 12,195. For the first time in DBIR history, software flaws (31%) surpassed stolen credentials as the leading initial access vector — and AI accelerating attacks from months to hours is the proximate cause.

Other findings: ransomware in 48% of confirmed breaches (up from 44%); median ransom payment dropped below $140,000; only 31% of victims paid. Third-party-involved breaches up 60% YoY to 48% of total. On AI: 67% of users access AI services from corporate devices using non-corporate accounts; 45% of employees are now regular AI users, up from 15% last year.

The patching crisis is the report's structural finding: only 26% of critical KEV vulnerabilities were fully remediated in 2025, down from 38% the previous year. Median resolution time increased by two weeks (43 days, up from 32), and organizations had 50% more critical bugs to patch than last year.

Why it matters: This is the year's anchoring industry baseline. Qualys, a DBIR research partner, frames it directly: this dataset may be an initial measurement of a "speed of light" for vulnerability remediation processes — a theoretical limit on what any model bound by human triage, change-windows, and approval gates can deliver. Sergej Epp's Zero Day Clock data lines up: in 2020 the disclosure-to-exploitation window was over 18 months; today it sits between 8 hours and 3 days.

Action for defenders: Re-anchor your vulnerability program on exposure window, not patch SLA. Pull your KEV remediation curve for the last 12 months and compare against the DBIR survival analysis. Prioritize by active exploitation, not CVSS. Treat shadow AI (67% bypassing corporate accounts) as a DLP problem today, not a future one.

4. Mini Shai-Hulud Supply Chain Worm Catches OpenAI, Mistral, UiPath, Guardrails — TeamPCP's Fourth Wave

Primary source: OpenAI disclosure · TanStack postmortem
Reporting: Wiz analysis · Snyk
Analysis: The Hacker News

What happened: On May 11, TeamPCP executed the fourth wave of the Shai-Hulud npm worm. The attacker published 84 malicious versions across 42 @tanstack/* packages between 19:20 and 19:26 UTC, chaining the pull_request_target "Pwn Request" pattern, GitHub Actions cache poisoning, and runtime extraction of an OpenID Connect (OIDC) token from runner process memory. The coordinated attack compromised over 170 npm packages and 2 PyPI packages, totaling 404 malicious versions — including Mistral AI's SDK suite, UiPath's automation tooling, OpenSearch, and Guardrails AI.

On May 15, OpenAI disclosed it had been caught: "Two employee devices in our corporate environment were impacted by this attack ... unauthorized access and credential-focused exfiltration activity, in a limited subset of internal source code repositories to which the two impacted employees had access."

The payload is built for cloud CI/CD. It steals GitHub tokens, npm tokens, AWS credentials (via IMDSv2), GCP and Azure credentials, Kubernetes service account tokens, HashiCorp Vault tokens, and environment variables — then identifies packages the victim has publish access to and propagates. It is also the first documented case of a malicious npm package carrying valid SLSA provenance.

Why it matters: SLSA provenance was a control model many cloud security programs were planning to lean on. TeamPCP just broke it. And as story #1 makes clear, this campaign was a dry run — the same playbook produced the GitHub internal repo breach nine days later.

Action for defenders: Treat any developer machine or CI runner that installed an affected package version on May 11 as compromised. Rotate every credential reachable from that host. Search for the gh-token-monitor daemon at ~/Library/LaunchAgents/com.user.gh-token-monitor.plist (macOS) or ~/.config/systemd/user/gh-token-monitor.service (Linux) and remove before revoking tokens, to avoid the wiper. Pin all GitHub Actions to full commit SHAs. Block git-tanstack[.]com and *.getsession.org at egress.

5. Grafana Confirms GitHub Codebase Theft After Coinbase Cartel Extortion Attempt

Primary source: Grafana Labs statement
Reporting: Cybersecurity Dive · The Record
Analysis: The Hacker News · SecurityWeek

What happened: On May 17, Grafana Labs publicly confirmed an unauthorized party used a compromised token to access its GitHub environment and download the company's codebase. The Coinbase Cartel extortion group listed Grafana on its leak site on May 15. Grafana refused to pay.

Grafana operates an open-source observability platform with more than 25 million users and 7,000 customers globally — including Nvidia, Microsoft, and Anthropic. No customer data or personal information was accessed during the attack.

CoinbaseCartel emerged in September 2025, assessed to be an offshoot of the ShinyHunters, Scattered Spider, and LAPSUS$ ecosystems. The group focuses purely on data theft and extortion, and has amassed 170 victims across healthcare, technology, transportation, manufacturing, and business services.

Why it matters: Three days before GitHub itself was breached through a different vector, Grafana lost its codebase through a stolen GitHub token. The trust model around source-code repositories is breaking down on multiple axes at once. Stolen source code remains risky because private repositories may contain internal logic, secrets, build processes, or unreleased features attackers can analyze for novel exploits.

Action for defenders: Inventory every PAT, app installation token, and OAuth grant against your GitHub org. Set token TTLs as low as your CI/CD architecture tolerates. Apply phishing-resistant MFA across all maintainer accounts.

6. GitHub Action Tag Hijack: actions-cool/issues-helper Compromised via Imposter Commits

Primary source: StepSecurity disclosure
Reporting: The Hacker News · SC Media

What happened: On May 18, StepSecurity disclosed that the widely used GitHub Action actions-cool/issues-helper had been compromised through a tag-redirection attack. "Every existing tag in the repository has been moved to point to an imposter commit that does not appear in the action's normal commit history," StepSecurity researcher Varun Sharma said. "That commit contains malicious code that exfiltrates credentials from CI/CD pipelines that run the action." A second action, actions-cool/maintain-one-comment, was hit the same way. The exfiltration domain has previously been observed in the Mini Shai-Hulud campaign, suggesting a potential link between the two activities.

Why it matters: The tag-redirection technique exploits the fact that most workflows pin to floating version tags (@v3) rather than full commit SHAs. Any workflow that references the action by version pulls the malicious code on its next run. Only workflows pinned to a known-good full commit SHA are unaffected.

Action for defenders: Audit every workflow for references to the compromised actions. Treat any CI run executing either as a credential exposure event. Implement an org-wide policy requiring SHA pinning for third-party GitHub Actions.

7. NYC Health + Hospitals: 1.8 Million Patients, Including Fingerprints and Palm Prints, Stolen via Third-Party Vendor

Primary source: NYC Health + Hospitals breach notice
Reporting: TechCrunch · TechRadar

What happened: On May 18, NYC Health + Hospitals disclosed a months-long breach exposing data on at least 1.8 million people. NYCHHC detected the attack on February 2 and secured its network; the hackers had been inside since approximately November 25, 2025 — more than two months of access before detection.

The breach is particularly sensitive because hackers stole biometric information, including fingerprints and palm prints, which affected individuals have for life and cannot replace. NYCHHC tied the intrusion to an unnamed third-party vendor.

Why it matters: Another data point in the DBIR's 60% YoY rise in third-party breach involvement — but with biometric data, where the consequences are permanent. A stolen Social Security number can be replaced. A compromised password can be changed. A fingerprint cannot.

Action for defenders: Audit which third-party vendors store biometric data on your behalf and what the recovery path looks like when one is breached. Force a real conversation with any vendor whose contract permits indefinite biometric retention.

🤖 8. Anthropic to Brief Financial Stability Board on Mythos Vulnerabilities — Bank of England Asked

Primary source: Financial Times reporting (May 18)
Reporting: PYMNTS · TechRadar

What happened: Anthropic agreed to meet with members of the Financial Stability Board (FSB) to discuss its Mythos model. The meeting was requested by Bank of England Governor Andrew Bailey, who is also an FSB member. Many FSB members have grown concerned that Mythos and AI models from other US tech companies could expose weaknesses in banks' cyber defenses. Anthropic said last month that Mythos had "found thousands of high-severity vulnerabilities, including some in every major operating system and web browser," with potential fallout for "economies, public safety and national security."

Why it matters: First time a sovereign-level financial stability regulator has formally engaged with an AI lab on the systemic risk of AI-assisted vulnerability discovery. For CISOs at financial institutions across the FSB's footprint, expect questions in your next exam about exposure window measurement and how your program scales if AI-discovered vulnerabilities arrive at 2–5× current volume in 2026–27.

🎯 Cloud Security Topic of the Week:

The Cybersecurity Verification Law — Why Offense Has a Superpower and What Defense Can Do About It

If you read this week's news brief and felt the underlying mechanic was familiar, Sergej Epp has a name for it: the Cybersecurity Verification Law. The argument goes like this. In every domain where AI has surged — chess, Sudoku, mathematics, benchmark coding — what made the surge possible was cheap verification. You can tell instantly whether the move worked, whether the proof is valid, whether the test passed. AI compounds capability fastest where the feedback loop is binary and cheap.

Map that principle onto cybersecurity and the picture is uncomfortable. Offense has cheap binary verifiers everywhere: you pop a shell or you don't, you capture the flag or you don't, you exfiltrate the secret or you don't. Defense has almost none. "Is this binary 56% malicious?" is not a verifier. "How confident is the SIEM alert?" is not a verifier. That asymmetry is structural — and it explains why offensive AI capability is sprinting ahead of defensive AI capability even when both sides have access to the same models.

That's the framing thread for this week's conversation with Sergej, who's spent 15 years inside cyber defense at Deutsche Bank, Palo Alto Networks, and now as CISO at Sysdig. [Listen to the full episode →]

Featured Experts This Week 🎤

Sergej Epp — CISO, Sysdig | Former CISO Deutsche Bank, Palo Alto Networks
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Cybersecurity Verification Law: Sergej's framing for why offensive AI is outpacing defensive AI. Wherever a domain has cheap binary verifiers (exploit worked / it didn't), AI compounds capability fast. Offense has these natively; defense doesn't.
Zero Day Clock: Sysdig's running measurement of the time between a CVE being disclosed and being exploited in the wild. In 2020 the window was over 18 months. As of this year it's between 8 hours and 3 days, depending on the cohort.
YOLO mode (Claude Code): Auto-approve mode in which the agent runs end-to-end without per-action confirmation. Sergej uses this as the offensive analogue for what defense now needs to build — autonomous response loops where the human is not in the per-event approval path.
Objective verifiers vs. environmental verifiers: Sergej's distinction. Offense owns objective verifiers (did the exploit fire?). Defense owns environmental verifiers (does this action match how my environment actually works — naming conventions, identity patterns, expected network flows?).
Honey tokens: One of the few cleanly binary signals on the defense side — a decoy credential or object that, when touched, conclusively indicates malicious activity.
Runtime security: Real-time detection and response inside production workloads, as opposed to point-in-time posture management or scanning. Sergej argues runtime telemetry is the ground truth that makes AI-driven defensive action reliable rather than hallucinated.

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💡Our Insights from this Practitioner 🔍

1. The Cybersecurity Verification Law — Where Offense Got Its Superpower

Sergej's opening move in the conversation reframes the AI security debate. The interesting question isn't who has the better model. It's where each side gets a clean signal that the model worked. "In every domain where you can measure something and verification is easy... AI is becoming very good. So all the benchmarks we've created so far across all the domains, AI was able just to reach 90, 95% of successes." — Sergej Epp

Apply that principle to security and the picture inverts. "It turns out offense is dominating. If you pop a shell, if you exploit, you get a very easy binary feedback. You get this feedback loop during the training, but also during the inference when AI agents are working — that yes, this happened really, now you've got this. Capture the flag, you've got this token, you've got the flag, the exploit worked. And so offense has this very cheap verifiers." — Sergej Epp

Defense doesn't have that. The signals are probabilistic, the alerts are noisy, the confidence scores are themselves model outputs. Sergej's blunt summary:"In defense, we don't really have this deterministic, binary verifiers. And therefore that explains a lot we're seeing right now — that offense is pretty much getting the superpower and accelerating much more compared to defense." — Sergej Epp

What to do with this: This is the lens that makes the rest of the conversation coherent. If your defensive program is being asked to "use AI" without first asking where your verifiers are, the AI investment compounds into the same noise floor you already had — just faster. The architectural work is making defense more like chess: identifying the places where you can produce binary signal, and building scaffolding everywhere you can't.

2. The Zero Day Clock — From 18 Months to 8 Hours

The Verification Law sounds abstract until Sergej puts a number on it. Sysdig publishes a running measurement called the Zero Day Clock that tracks the time from CVE disclosure to active exploitation. "It was more than one and a half years... And now we are like under a day. Under, under 24 hours. I think it's just varying between eight hours and one to three days... You can expect this is going to drop to minutes or hours." — Sergej Epp

The mechanism is mundane and powerful at the same time. "Effectively when a vendor ships a patch, it ships the blueprint of the vulnerability. And the AI is really good right now in reconstructing this blueprint and building the exploit out of this blueprint. So right now, as a bad nation state, you can just deploy a lab and then collect all these different patches and instantly create exploits." — Sergej Epp

The patch is the disclosure. The diff is the spec. AI-assisted analysis turns N-day vulnerabilities into working exploits inside the time most enterprises take to schedule a change advisory board meeting. That maps directly onto Verizon's DBIR finding this week: only 26% of critical vulnerabilities were fully remediated by organizations in 2025, compared to 38% the previous year. The defenders aren't getting worse. The window is.

Cloud security takeaway: If your patch SLA is 30/60/90 and your change-management window is two weeks, you're now permanently out of band with the threat.

3. The 8-Minute AWS Compromise — Why Speed of Attack Forces Speed of Defense

The Verification Law explains the trajectory. Sergej then drops the operational example that grounds it. "We just saw recently — we detected one AWS environment being compromised. The attacker moved from zero — he just got some stolen credentials — to full admin in eight minutes." — Sergej Epp

The attacker was using AI, and Sergej's team could tell because of the artifacts the AI left behind: "He was assuming roles which were called 'Claude'. Whenever the AI was stuck — for instance trying to spin up some GPU to mine cryptocurrency — when it was stuck, it was just trying to call an Anthropic GitHub repo which didn't exist." — Sergej Epp

Eight minutes from stolen credential to full admin. Sergej's framing of what this requires: "How is a SOC analyst supposed to cope with an attack which is taking under 10 minutes? We have to take the human out of the loop." — Sergej Epp

The link back to story #3 in the news brief: the DBIR doesn't just say patching is slow. It says the speed-of-defense ceiling is hitting a wall that human triage and approval cycles can't break through.

4. What Defenders Actually Have — Honey Tokens and the First-Principle Advantage

If the Verification Law makes defense sound hopeless, Sergej's deeper point is that it isn't — defense just has different verifiers than offense, and most programs aren't using them.

The cleanest binary signal on the defense side: "Honey tokens. If you look at any EDR, XDR solution — all of them are throwing around honey tokens to detect ransomware. Because it's the only unique binary signal that something is happening. Somebody's eliminating these honey tokens. Same in the cloud." — Sergej Epp

The first-principle advantage: "The attackers do not understand the environment. So every time they're just going to go in and reach the objective... they're going to start to perform steps from scratch based on the training data. So they will try to assume certain roles with certain usernames, based on trying to hallucinate down something. And that's going to create a lot of noise. You can hear this noise — you can start build your detection around that." — Sergej Epp

The 8-minute AWS incident is the proof point. The attacker's AI hallucinated Claude-named roles and reached for a non-existent Anthropic GitHub repo because it had no idea what the actual environment looked like. That noise is detectable — but only if the defender has done the work of mapping their environment first. "If you can explain how your environment is looking like — for instance, what kind of naming conventions you're using for your clusters, what kind of naming conventions you're using for your identities — starting to understand that, equipping your team with this understanding, building detection rules on top of that, is quite powerful. And that's by the way, something also vendors will not be able to help with. Because that's your unique experience, insights you have as a company." — Sergej Epp

Cloud security takeaway: Build the environment graph and the naming-convention catalog as a first-class artifact, not an afterthought. The detection signal that catches a TeamPCP-style intrusion is unlikely to be a CVE signature — it's a service account assuming a role with a name that doesn't exist in your taxonomy.

5. Runtime Security as the Ground Truth

If detection has to happen inside the attack window, the data layer that supports it has to be real-time and high-fidelity. "If a hack is happening within eight minutes, your inventory posture management is not going to help. Whatever's just misconfigured, you'll not be able to go back, send this, open up a ticket in Jira and have the engineer just work on that." — Sergej Epp

Why runtime, not posture: "To have this ground truth — what's going on. Because if you just have parts of it — five or six events suggesting, oh, something happened in this Kubernetes container — but I don't really know what processes were running, I don't really know if container escapes were performed. If I don't have a lot of this telemetry, I'm not going to be confident to say 'I'm going to kill this container.'" — Sergej Epp

The architectural prescription: runtime telemetry produces ground truth, deterministic rules produce binary signal, and only then does AI come in to reason across the noise. This inverts how most "AI for SOC" pitches are structured — and it lines up with where the news brief landed: Mini Shai-Hulud, the actions-cool tag hijack, and the GitHub VS Code extension breach all depended on activity inside developer or CI/CD workloads that posture scanning can't see in real time.

6. AI Agent Risk — Simon Willison's Three Categories

When the conversation pivots to securing AI agents inside the enterprise, Sergej grounds it in a mental model from security researcher Simon Willison. Agents do three things; the safe configuration takes at least one off the table. "What kind of access to data is the AI agent having? Can the AI agent execute commands? Does the AI agent have access to the internet? Simon Willison posted about this recently — you have to take at least one of these out. Otherwise it's going to be a huge blast radius and could lead to a nightmare scenario." — Sergej Epp

Sergej is realistic about how hard this is in cloud: "Even if you take one of those aspects out, you can't really take out the network capability in the cloud. Let's say you're going to say it cannot run commands — I'm even not sure that's possible, because even if you analyze a PDF or whatever, the AI is going to write a script and then the script is going to do something." — Sergej Epp

His operational fallback is the same as for any other privileged workload: runtime visibility into every LLM and coding agent in production.

Cloud security takeaway: Apply Willison's three-category test to every AI agent deployment before production. If your coding agent has data access, execution rights, and network egress, document why and what control compensates.

7. Two Team Archetypes — Architects of Security and Validators of Security

The conversation moves to organizational design. Sergej's argument is that the discipline-based team structure (cloud security, AppSec, IR, etc.) doesn't survive AI-speed operations. What replaces it is a two-archetype split. "We're going to see two types of roles going forward. The ones where you have all the security engineering, forensics coming together — the architects of security. And the validators of security: people who are building a validation architecture. Trying to understand: now I've got these controls — what kind of ground rules, what kind of data are those controls collecting? Is this EDR really in the position to explain this type of attack and reconstruct it back? Are these rules really validated?" — Sergej Epp

The third leg is the operating assumption: "You have simply to assume breach. That's the reality we are living in. And based on that, you build up your runtime, real-time controls — where you take the human out of the loop." — Sergej Epp

Cloud security takeaway: If your current org chart has a discipline-based shape (one team per technology), start thinking about how to overlay an architect/validator split on top of it. The validator role specifically — the team whose job is to continuously verify that your detections and controls actually fire on the attacks you claim they cover — is the one most programs don't have today.

8. The Sysdig Hackathon — Why Culture Beats Mandate for AI Adoption

The most practical part of the conversation is also the most underrated. Sergej described running an internal hackathon to let his security team build with AI rather than mandating tool adoption from the top. "A lot of companies are trying to mandate from the top — you have to use AI, this is the tool you have to use. I think the first thing AI is going to disrupt is the entire management layer. Because the experts understand the pain points, they understand the problem. So let them try out how the AI is working, let them try to fix these problems." — Sergej Epp

The output surprised him: "I had a lot of IT people who didn't have an understanding of security, and a threat researcher who never wrote a line of code. And she was able then to come up with a framework to check if any APIs of Azure had drift, and how to adopt automatically the rules on top of that." — Sergej Epp

Cloud security takeaway: A half-day cross-functional hackathon — explicitly framed as exploration, not delivery — surfaces use cases your org chart doesn't predict.

🧠 Mental Model — Offense Owns Objective Verifiers. Defense Owns Environmental Verifiers.

Offense knows when the exploit worked. Defense doesn't natively know when the detection was right.

The program that wins in 2026 is the one that asks, for every control in its stack, "what's the binary signal that this fired correctly?" — and replaces the controls where the answer is "we don't really know."

Honey tokens give you that signal. Environment graphs give you that signal. Naming-convention catalogs give you that signal. CSPM dashboards mostly don't.

Zero Day Clock — Sergej's running dashboard tracking the gap between CVE disclosure and active exploitation
Sysdig Strategic Research — Cloud threats and runtime security publications
Verizon 2026 DBIR — Full report and industry-specific breakdowns
CISA Known Exploited Vulnerabilities Catalog — Authoritative list of what's being exploited
Simon Willison's writing on AI agent risk — The three-category mental model Sergej references
OWASP LLM Top 10 — For teams building the AI agent governance layer

Podcast Episode

[Full Episode with Sergej Epp (Sysdig)] — Complete transcript and audio for this week's featured conversation

Question for you? (Reply to this email)

🤔 If you mapped your defensive controls against Sergej's Verification Law tomorrow, which ones produce binary signal — and which are just probabilistic noise dressed up as detection?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

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🚨 Google Stops the First AI-Generated Zero-Day - Why "Guardrails Are Dead"

Ashish Rajan — Wed, 13 May 2026 22:39:48 +0000

This week’s Cloud Security Newsletter topic: Guardrails Are Dead — What Replaces Them in the Agentic Era (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

The week of May 8–13 produced two stories that sit at exactly the same intersection: AI is now both the attacker's tool and the attack surface. Google Threat Intelligence Group disrupted the first documented case of a criminal actor using an AI-generated zero-day exploit. Microsoft published research showing how prompt injection in its own Semantic Kernel framework escalates to host-level remote code execution in two separate code paths. And TeamPCP's Mini Shai-Hulud worm, which the same Google report attributed to UNC6780, compromised 172 packages across 403 versions, including the first malicious npm package ever to carry valid SLSA provenance.

This week's conversation is with David Haber, VP AI Security at Check Point and founder of Lakera (the team behind Gandalf, the AI red-team game that has logged over 100 million human-AI interactions), and Paul Barbosa, VP of Cloud and SASE at Check Point, hosted by Ashish Rajan. The framing is sharp: David's position, "I believe guardrails are dead," is the editorial spine of this week's news as much as it is of the episode. [Listen to the episode]

⚡ TL;DR for Busy Readers

- Mini Shai-Hulud (CVE-2026-45321) compromised 172 npm/PyPI packages including TanStack, Mistral AI, UiPath, and OpenSearch. First malicious npm package with valid SLSA provenance. Rotate every secret that touched affected pipelines May 10–12.

- Google disrupted the first AI-generated zero-day: a Python 2FA bypass authored by an LLM and identified before mass exploitation. Assume exploit dev is now faster than your patch cycle.

- Microsoft Semantic Kernel CVEs (CVE-2026-26030, CVE-2026-25592) turn prompt injection into host RCE. If your agent calls tools, prompt injection is now a code-execution problem with blast radius equal to the agent's IAM.

- PAN-OS CVE-2026-0300 (CVSS 9.3) actively exploited by a likely state-sponsored cluster. Restrict Captive Portal to internal zones now; patches start May 13.

- David Haber's central argument: Stop layering perimeter guardrails. Move to contextual intelligence: evaluating agent intent, system instructions, and behavioral traces against what the agent is currently doing.

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. Google Disrupts the First Documented AI-Generated Zero-Day

Primary source: Google Cloud Threat Intelligence
Reporting: Bloomberg

What happened: On May 11, Google Threat Intelligence Group disclosed the first known case of a criminal threat actor using an AI-generated zero-day exploit: a Python script that bypassed two-factor authentication on a popular open-source, web-based system administration tool. GTIG identified the exploit before mass exploitation and attributes AI authorship with high confidence based on hallucinated CVSS scores, abundant educational docstrings, and textbook Pythonic formatting. Gemini was not the LLM used. The same report documents North Korea-linked APT45 running thousands of recursive prompts to validate PoC exploits at scale, China-linked UNC2814 using persona jailbreaks ("act as a senior security auditor") to research TP-Link firmware flaws, and a Chinese actor deploying agentic offensive tools Strix and Hexstrike against East Asian targets.

Why it matters: This is the operational confirmation defenders have been forecasting for two years. AI-assisted vulnerability discovery has moved from theory to a working capability in the wild. For cloud security teams, the implication is concrete: if a single LLM-augmented researcher can produce weaponizable exploits faster than your patch cycle can absorb them, patch SLAs alone are no longer a viable program. Weight has to shift toward compensating controls: behavioral detection, least-privilege scoping for service accounts and non-human identities, and rapid containment playbooks that work without a patch in hand. The 72-minute breakout-time benchmark Unit 42 cited earlier this year now looks like the floor, not the ceiling.

2. Mini Shai-Hulud Worm: 172 Packages Compromised, Including the First Malicious Package with Valid SLSA Provenance 🚨

Primary source: Wiz · Snyk
Advisory: NHS England Cyber Alert
Analysis: The Hacker News

What happened: Between 19:20 and 19:26 UTC on May 11, TeamPCP (tracked as UNC6780 in Google's report this week) published 84 malicious versions across 42 @tanstack/* npm packages in under six minutes, then propagated to Mistral AI, UiPath, OpenSearch, Guardrails AI, and PyPI within hours. By May 12 the campaign had hit 172 unique packages across 403 malicious versions with cumulative downloads exceeding 518 million. TanStack's compromise was assigned CVE-2026-45321 (CVSS 9.6). The attack chain hijacked GitHub Actions via a pull_request_target trigger, used cache poisoning, and extracted OIDC tokens from /proc memory on the runner. npm tokens were never stolen; the publish pipeline itself was compromised. Payloads exfiltrate AWS IAM keys, GitHub PATs, HashiCorp Vault tokens, Kubernetes secrets, and 1Password/Bitwarden vaults, and inject persistence hooks into Claude Code (.claude/settings.json) and VS Code (tasks.json with runOn: folderOpen).

Why it matters: This is the most consequential supply chain attack of 2026 to date, and it breaks an assumption cloud security programs were starting to rely on: SLSA provenance attestation no longer guarantees a package wasn't tampered with. Any CI/CD environment that mints OIDC tokens (i.e., most modern GitHub Actions pipelines) is in scope. The persistence vector into AI coding agents is novel and underappreciated: a single .claude/settings.json injection turns a developer's daily AI assistant into a sustained execution channel. This is also the precise scenario David Haber describes in this week's episode: agents with tool access, untrusted inputs, and the autonomy to act.

3. Microsoft Research: Semantic Kernel Prompt Injection Becomes Host-Level RCE

Primary source: Microsoft Security Blog — Semantic Kernel research disclosure Advisory: CVE-2026-26030 (Python semantic-kernel < 1.39.4) · CVE-2026-25592 (.NET SessionsPythonPlugin) Analysis: Vibe Graveyard · Windows Forum

What happened: On May 7, Microsoft disclosed and patched two CVEs in its Semantic Kernel AI agent framework. CVE-2026-26030 (critical, Python semantic-kernel < 1.39.4) is in the In-Memory Vector Store / Search Plugin path: a single crafted prompt launches calc.exe on the agent's host machine when the agent uses the default filter functionality. CVE-2026-25592 is in the .NET SDK's SessionsPythonPlugin, which is meant to isolate Python execution inside Azure Container Apps dynamic sessions but exposed a sandbox-to-host file-transfer helper as a kernel function. Once the LLM could invoke it as a tool, the local file path became attacker-controlled, enabling arbitrary host file writes from inside what was supposed to be an isolated sandbox. Microsoft framed the research as a class problem, not a one-off.

Why it matters: This is the cleanest existing demonstration that AI agent security cannot be reduced to prompt safety. When an agent can call functions, query stores, run scripts, or touch cloud APIs, prompt injection is an application-security and identity problem with blast radius proportional to the agent's privileges. The story lands the same week David Haber argues, for unrelated reasons, that guardrail-style perimeter defenses are no longer sufficient against exactly this class of attack. The Microsoft research is the proof.

4. PAN-OS Captive Portal Zero-Day Actively Exploited — Likely State-Sponsored 🚨

Advisory: Palo Alto Networks Advisory
Analysis: Unit 42 Threat Brief · Wiz
Reporting: The Hacker News

What happened: Palo Alto Networks disclosed CVE-2026-0300 (CVSS 9.3) on May 6, with patches starting May 13. The flaw is an unauthenticated buffer overflow in the PAN-OS User-ID Authentication Portal (Captive Portal) on PA-Series and VM-Series firewalls, enabling RCE with root privileges via crafted packets. Prisma Access, Cloud NGFW, and Panorama are unaffected. CISA added it to KEV on May 6 with a May 9 federal patching deadline. Unit 42 attributes limited observed exploitation to CL-STA-1132, a likely state-sponsored cluster that achieved RCE on April 16 after a week of unsuccessful attempts beginning April 9, injected shellcode into an nginx worker, deployed EarthWorm and ReverseSocks5 tunneling tools, and conducted Active Directory enumeration using the firewall's service account credentials.

Why it matters: A second story this week reinforcing the same theme as #1 and #2: internet-exposed security infrastructure is a primary target, not a hardened boundary. The post-exploitation pattern (pivot from the firewall's service account into AD enumeration) is textbook state-sponsored playbook and reinforces why firewall service accounts deserve tier-zero-equivalent treatment in your identity model. Approximately 225,000 internet-facing PAN-OS instances exist globally per Shodan, though only a subset run Captive Portal on ports 6081/6082.

5. Instructure Pays Ransom to ShinyHunters After Double Canvas Breach Hits 275M Users

Primary source: Inside Higher Ed · The Register
Reporting: The Hacker News

What happened: Instructure, parent of the Canvas LMS used by ~41% of North American higher-ed institutions, confirmed on May 11 it reached an agreement with ShinyHunters after a two-stage breach. The initial intrusion on April 29 (disclosed May 1) exploited a flaw in the Canvas Free-for-Teacher account program to exfiltrate 3.65 TB of data: ~275 million records across 8,809 institutions including Harvard, Princeton, Columbia, Stanford, Penn, and Georgetown. After Instructure declined to negotiate and attempted to patch, ShinyHunters returned on May 7, defaced ~330 Canvas login portals worldwide, and took the platform offline during US finals week. Instructure subsequently stated it received "digital confirmation of data destruction (shred logs)," a strong implication of a ransom payment the company has not explicitly confirmed. The Free-for-Teacher program has been permanently shut down.

Why it matters: This is the largest education-sector breach on record and a textbook SaaS concentration-risk case. A single under-governed product feature gave the adversary a path into the production multitenant Canvas environment. ShinyHunters' 2026 playbook (third-party integrator compromise to reach downstream customers at scale) builds on its 2024 Snowflake-customer campaign and 2025 Salesforce campaign. The broader question for enterprise CISOs: many organizations now depend on a small number of critical SaaS platforms but lack mature playbooks for tenant compromise, platform-wide outage, or third-party-driven data exposure. The gaps this incident exposes for ed-tech are equally true for HR, CRM, identity, and collaboration SaaS.

🎯 Cloud Security Topic of the Week:

Guardrails Are Dead - What Replaces Them in the Agentic Era

The thread running through every story above is the same: attackers no longer need to break the perimeter when they can manipulate the systems that operate inside it. The Google-disrupted AI zero-day worked because LLMs collapse the cost of producing weaponizable exploits. The Mini Shai-Hulud worm worked because trusted CI/CD primitives (OIDC tokens, SLSA attestations, GitHub Actions triggers) could be weaponized inside a pipeline that defenders had explicitly designed as trusted. The Semantic Kernel CVEs work because an agent's tool-calling layer is, by design, a privileged execution channel that text from anywhere can reach.

David Haber and Paul Barbosa's argument is that the security industry's response to this class of problem, layering more guardrails, more perimeter checks, more "don't do that" rules at the prompt level, is structurally finished. What replaces it is something Haber calls contextual intelligence: evaluating intent, system instructions, behavioral traces, and tool calls in real time against what the agent is actually supposed to be doing. It is a harder problem and a different operating model.

Featured Experts This Week 🎤

David Haber — VP AI Security, Check Point | Founder & former CEO, Lakera (creators of Gandalf)
Paul Barbosa — VP, Cloud and SASE, Check Point
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Direct prompt injection: User input crafted to override an LLM's system instructions. The original form ("ignore your previous instructions and do as I say") exploits the fact that LLMs do not architecturally distinguish between system instructions, retrieved data, and user input. It is all text and tokens.
Indirect prompt injection: Malicious instructions delivered to an agent via the data and tools it consumes: a shared document, an email, an MCP-connected drive, an output from another agent. The victim does not interact with the malicious content; the agent does. As David Haber puts it, indirect prompt injections are often invisible, both hard to spot in real time and undetectable after the fact.
Gandalf: Lakera's open-source AI red-teaming game launched ~2.5 years ago. Has reached tens of millions of people and logged over 100 million human-AI interactions, one of the largest datasets of how people actually try to exploit LLMs.
Contextual intelligence (as a defense pattern): The replacement Haber proposes for perimeter guardrails. Evaluates the agent's design, system instructions, behavioral traces, and current actions in real time to reason about whether the agent is being manipulated. Requires substantially more telemetry than guardrail-based defenses.
Non-human identity (NHI): Service accounts, API keys, OAuth tokens, and AI-agent identities. The credentials that AI agents and automation use to act inside cloud and SaaS environments.
Language as the new executable: Paul Barbosa's framing that the domain of exploitation has shifted from code (requiring CS expertise and tool fluency) to natural language (bounded only by human creativity).

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💡Our Insights from this Practitioner 🔍

1. The Threat Model Shift: Language Is the New Executable

The opening frame of the conversation is one cloud security leaders should sit with. Paul Barbosa describes the moment he understood why prompt injection is qualitatively different from prior classes of vulnerability:

"The domain of exploit was very code driven. You had to know what you were doing. But with prompt injection, it's language. And we're only bound by like the limits of human creativity, which we know is boundless." — Paul Barbosa

The implication is that the population of people who can produce a working exploit has expanded by orders of magnitude. Haber confirms this empirically from Gandalf's 100M+ interaction dataset:

"The most beautiful example we see — 12-year-old kids that are very successful playing the game. And we see some of the most advanced hackers that are also very successful at playing the game." — David Haber

That observation is the editorial bridge to this week's Google Threat Intelligence report. AI-assisted vulnerability research is not theoretical for state actors. APT45's recursive PoC validation runs and the criminal actor who produced the disrupted 2FA bypass are both empirical confirmations of what Haber has been seeing from the offensive side for two years.

What to do with this: Audit which assumptions in your threat model still depend on "attacker scarcity," the idea that sophisticated attacks require sophisticated attackers. The assumption no longer holds. Programs that rely on it (vulnerability triage that deprioritizes anything not on KEV, MFA selection that treats SMS as adequate, AppSec coverage that assumes attackers won't fuzz a particular surface) need an explicit refresh.

2. Indirect Prompt Injection Is the Attack That Matters

Haber draws a sharp distinction between direct and indirect prompt injection that maps directly onto how security teams should weight their concern:

"I can exfiltrate your entire corporate inbox in about three seconds while you are on vacation, sipping a mojito. You will not even notice that I did that through an indirect prompt injection. You will have no idea. The indirect ones are often invisible. They're not only hard to spot — but also after the fact, you wouldn't even know." — David Haber

The Check Point team demonstrated this with a now-canonical example: a Google Doc containing a malicious prompt, shared with a user who never opens it. The user's AI agent, connected to their Drive, reads the document, follows the injected instructions, and exfiltrates data. The victim is not in the interaction loop at any point.

This is the exact attack class Microsoft's Semantic Kernel research validates this week. The agent has tools. The tools have privileges. Any text the agent processes (including text it was asked to summarize, retrieve, or analyze) can become an instruction. The blast radius is determined entirely by what the agent is permitted to do.

What to do with this: Inventory every AI agent in your environment by what data it reads and what tools it can call. Any agent that ingests untrusted content (email, shared documents, web pages, MCP-connected drives, tickets) and also has write or execute privileges is a candidate for indirect prompt injection. Reduce one side of the equation or the other; most agents are over-permissioned on tools relative to what they actually need.

3. Guardrails Are Dead, and Why That Matters Now

This is the central thesis of the conversation, and Haber is unambiguous:

"Last year, the hot talk in town was guardrails. I believe guardrails are dead. With the autonomy and the complexity that agentic AI brings, we need to go away from what are essentially perimeter checks. Putting one guardrail after another — 'don't talk about weapons, no hate speech, don't bash the competitor, prompt injection defense' — we've been layering on these guardrails on top of AI. That's over. It doesn't scale. What we need to do now is we need to move from perimeter checks to contextual intelligence." — David Haber

Contextual intelligence, in Haber's framing, evaluates the agent's design intent, system instructions, traces from past behavior, and user analytics in real time against the action the agent is currently taking. It is reasoning about whether the agent is being manipulated, not a static rule about what it can and cannot say.

The structural critique applies just as cleanly to traditional cloud security tooling. Barbosa surfaces the WAF analogue:

"How do you detect the prompt injection through a WAF? It's impossible. So if that's the access modality — that's one of the first places that we chose to make the integration with Lakera and the runtime security — to augment the WAF. Otherwise it's a request and there's no existing method to try to detect it." — Paul Barbosa

The point is not that WAFs are obsolete. It is that the layer in your stack designed to inspect text-shaped requests has no native concept of "is this text trying to manipulate a downstream model?" Adding that capability is an architectural change, not a rule update.

What to do with this: If your current AI security strategy is a list of prompt filters or content guardrails bolted to model APIs, treat that as a starting baseline and not the end state. The investments that actually scale are agent-level behavioral instrumentation, tool-call auditing, and runtime evaluation of agent actions against declared intent. These are heavier engineering lifts than guardrails, and they need to start now.

4. Identity for Agents Is Not a Solved Problem

When asked whether non-human identity controls can carry the weight that guardrails no longer can, Haber's answer is direct. His position, paraphrased from the transcript (where one word appears garbled and is preserved as such below):

"I don't think the identity prompt [transcript reads "prompt"; almost certainly "problem"] for agents has been solved. At all... There are certain important questions around how we want to treat agents... One of the big areas people are looking into is self-replicating agents. So you've got teams that are replicating themselves to maybe do other tasks. How do identities evolve with that? I don't think that's clear at all. Many claim they've solved it. I've not seen anything that convinces me that we have a good handle on that." — David Haber [VERIFY exact word — possible transcription error]

Barbosa reinforces the point. Least privilege for NHIs is necessary but not sufficient, because the space is moving too fast for any static control to be a stopping point:

"It's never gonna be enough. The space is evolving too fast for any static control to say, okay, I understand I have non-human identity and I'm gonna apply least privilege. That's just table stakes." — Paul Barbosa

This connects directly to the Mini Shai-Hulud worm and the Semantic Kernel CVEs. The worm exfiltrates exactly the credentials agents and automation use (AWS IAM keys, GitHub PATs, Vault tokens) and uses them to expand. The Semantic Kernel research shows that the agent's own service identity is the blast radius. Least privilege bounds the damage; it does not prevent the path.

What to do with this: Treat NHI governance as a year-long program, not a project. The minimum tactical baseline: every AI agent in production has its own service identity (not a shared one), the identity is scoped to the specific tools and data the agent needs, and there is logging that tells you when the identity is used outside its expected pattern. Beyond that, plan for the harder problems Haber names: identity for self-replicating agents, identity that travels across environments, distinguishing "Ashish acting via an agent" from "an agent acting on Ashish's behalf."

5. The Same Action, Catastrophic by Context

Barbosa's framing of why context-aware defense matters is the cleanest articulation of the agentic security problem:

"The same action that an agent could take could be okay — or it could be catastrophic, just depending on the conditions. The constraint on AI is never gonna be security, unfortunately. The constraint is gonna be productivity — and productivity by its very nature is always to be more helpful, is gonna ask for more and more access, more and more authorization. And I think as humans we're gonna gladly grant that. That same action taken by an attacker could be catastrophic." — Paul Barbosa

This is the exact dynamic in the Semantic Kernel SessionsPython case: a file-transfer helper is benign in the workflow it was written for, and an arbitrary host write primitive in the hands of an injected prompt. The function did not change. The context did.

What to do with this: When evaluating agent deployments, the question is not "does this tool seem dangerous?" but "what is the worst action this tool enables if the agent is being manipulated by content it just ingested?" Most production agents have not been audited against that question. Start with the agents that touch customer data or cloud control planes.

6. AppSec, Network, Data — It's All One Problem Now

The conversation closes on an organizational point that maps directly onto how this week's news has to be triaged. Ashish presses on whether AI security is an AppSec problem or a data security problem. Haber's answer:

"Now it's everything." — David Haber

Barbosa expands on what that means operationally:

"It used to be like they own the tool — they're the network security team, they got the firewall, we'll get a ServiceNow ticket, it'll go to them. Now I think everyone more than ever, it's everyone's problem. If I'm a CISO, I'm going to every domain that I have a tool set and saying — how are you solving for this? Because it can render itself through your control, your tool set, or the applications that you're protecting." — Paul Barbosa

The Mini Shai-Hulud worm illustrates the point. The story is simultaneously a CI/CD problem (poisoned GitHub Actions), an identity problem (OIDC token theft), a secrets problem (Vault and IAM exfiltration), an endpoint problem (persistence into Claude Code and VS Code config), and a SaaS problem (npm and PyPI as the distribution channel). No single domain owner can fully respond to it.

What to do with this: This week is a forcing function to ask, across every security domain in your organization, the same question Barbosa describes: "how are you solving for this?" If the answer is "we're not, that's another team," find the gap and own it.

7. The Defense Window Is Open, But It Is Closing

Haber's closing observation is the one worth carrying into next week's planning:

"We are actually at a very unique time, I believe right now, where we still have a chance for defense to catch up. I see both offensive security and defense on an exponential curve. But I think the question is — what's the exponent? How fast are we actually moving?" — David Haber

The Google-disrupted exploit was caught because GTIG was looking; the next one may not be. Mini Shai-Hulud broke an attestation primitive (SLSA) that defenders were starting to trust. Microsoft framed the Semantic Kernel research as a class problem, not a one-off.

What to do with this: Pick one of the harder problems — agent behavioral monitoring, NHI lifecycle management, CI/CD supply chain attestation that survives runner compromise — and make actual progress on it this quarter.

🧠 Mental Model — Language Is the New Executable

If language is executable, then every place an agent reads untrusted data is a place an attacker can run code.

Guardrails were the AV scanner of the LLM era: pattern-matching on a target that mutates faster than the patterns. The successor is not a better filter. It is runtime context — what was this agent told to do, what is it doing now, and does the second match the first?

Cloud security teams that build that telemetry layer in 2026 will be the ones positioned to defend in 2027. Teams that keep adding guardrails will be running an antivirus strategy against an autonomous adversary.

Check Point AI Security research — AI security threat research and Lakera-related publications
Gandalf by Lakera — gandalf.lakera.ai — AI red-team game, educational tool for prompt injection patterns
CISA Known Exploited Vulnerabilities Catalog
Google Cloud Threat Intelligence — AI Threat Tracker (May 2026)
Microsoft Security Blog — Semantic Kernel research disclosure
Unit 42 — PAN-OS CVE-2026-0300 Threat Brief
Wiz / Snyk — Mini Shai-Hulud analyses

Podcast Episode

Full Episode with David Haber and Paul Barbosa (Check Point) — Complete transcript and audio for this week's featured conversation

Question for you? (Reply to this email)

🤔 If guardrails are dead, what is the first thing you take out of your AI security stack — and what do you put in its place?

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Claude Mythos broke vulnerability management in 72 hours

Ashish Rajan — Thu, 07 May 2026 21:21:00 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Why CVSS Alone Won't Survive the AI Era (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

If the past week had a thesis, it's this: the gap between vulnerability disclosed and vulnerability weaponized is no longer measured in months. It's measured in days, sometimes hours, and the underlying cause isn't a single tool or actor — it's the AI-assisted offensive workflow that's now table stakes for advanced adversaries.

The news brief reflects it everywhere. Palo Alto Networks disclosed CVE-2026-0300 with state-sponsored exploitation already underway. Microsoft documented a three-day AiTM campaign that quietly stole post-MFA tokens from 13,000 organizations. Cyera's Bleeding Llama disclosure showed how default-permissive AI infrastructure is leaking the most sensitive secrets in the building. And Cisco put $400 million on the table to lock down non-human identities — the credential layer AI agents now use to act inside enterprises.

Against that backdrop, this week's conversation is with Brad Hibbert, COO and Chief Strategy Officer at Brinqa, hosted by Ashish Rajan of Cloud Security Podcast. The discussion is about Claude Mythos — Anthropic's frontier model now being tested in private programs against vulnerability discovery — and what it means for every existing vulnerability management program. Brad's framing is sharp: "Heartbleed was a storm. Mythos is climate change." [Listen to the episode]

⚡ TL;DR for Busy Readers

This week’s attacks didn’t break systems — they used them


🔥 PAN-OS CVE-2026-0300 is being actively exploited by a likely state-sponsored cluster — restrict Captive Portal to internal IPs now; patches start May 13.

🪪 Microsoft AiTM campaign stole post-MFA tokens from 35K users — non-phishing-resistant MFA is over; move M365 admins to FIDO2/passkeys this quarter.

🦙 Bleeding Llama (CVE-2026-7482) leaks heap memory from 300K Ollama servers, including API keys and prompts — inventory, upgrade to 0.17.1, rotate exposed secrets.

🤖 Cisco's $400M Astrix acquisition validates non-human identity as a top-tier security category — start your NHI inventory this month.

⏱️ Brad Hibbert's core argument: Stop measuring patch SLAs. Start measuring the exposure window — how long a vulnerability was actually exploitable in your environment..

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. PAN-OS zero-day: state actors are already inside

Palo Alto Networks disclosed CVE-2026-0300 on May 6 — unauthenticated RCE on PA-Series and VM-Series firewalls. Unit 42 is tracking active exploitation by CL-STA-1132, a likely state-sponsored cluster. The pattern: RCE → log destruction → AD enumeration via firewall service account credentials.

CISA added it to KEV on May 6. Patches roll out May 13. Until then: restrict the User-ID Authentication Portal to internal IPs only.

The attackers had RCE for days before disclosure. Any defender measuring success by patch SLA had a green dashboard while their AD was being mapped, a textbook example of why "exposure window" matters more than "patch window."

→ Read the Unit 42 brief

2. 35,000 users. MFA didn't help.

Microsoft Defender Research disclosed a 3-day adversary-in-the-middle campaign (April 14-16) that hit 35,000+ users across 13,000 organizations. Healthcare and finance led the target list. The attackers proxied the legitimate Microsoft login flow in real time, capturing post-authentication session tokens — sidestepping passwords and SMS/app-based MFA entirely.

If you're still on non-phishing-resistant MFA for M365 admins, this is your wake-up call. FIDO2 or passkeys this quarter, not next.

→ Read Microsoft's analysis

3. Your AI inference servers are leaking secrets

Cyera's "Bleeding Llama" disclosure (CVE-2026-7482, CVSS 9.1) is the AI infrastructure story most cloud teams aren't tracking. Three unauthenticated API calls leak the entire Ollama process memory — including the API keys, database creds, and cloud secrets sitting in environment variables on your inference hosts.

300,000 servers exposed. Patched silently in 0.17.1, but the patch notes never flagged it as a security update — so most operators never upgraded.

The pattern: AI infrastructure deployed with localhost-tool defaults running as production servers, with the most sensitive credentials in the building sitting in 0.0.0.0-bound process memory.

→ Read Cyera's writeup

4. Cisco bets $400M that non-human identity is the new perimeter

Cisco announced its intent to acquire Israeli identity-security startup Astrix Security for ~$400M on May 4. Astrix discovers and governs non-human identities (NHIs) — API keys, service accounts, OAuth tokens, machine credentials — across their lifecycle. Cisco will fold it into Identity Intelligence, Duo, Secure Access, and Splunk.

This is the first nine-figure security M&A explicitly framed around securing AI agents at the credential layer. Machine identities outnumber humans 10-to-1 in most enterprises, and they're the path of least resistance for AI-agent compromise and supply chain attacks.

Expect rapid consolidation across Okta, CyberArk, SailPoint, Wiz, and Palo Alto. Inventory NHIs now if you haven't.

→ Read SecurityWeek's coverage

🎯 Cloud Security Topic of the Week:

From Patch Windows to Exposure Windows — Why CVSS Alone Won't Survive the AI Era

The shift Brad Hibbert describes is structural, not tactical. For two decades, vulnerability management has run on a clear contract: a vendor publishes a CVSS score, your scanner picks it up, you classify by severity, and you remediate within an SLA — typically 30/60/90 days driven by PCI or another compliance regime. Patch SLA performance became the metric that boards and auditors rallied around.

That contract assumed two things that no longer hold: (1) attackers needed time and skill to weaponize disclosed vulnerabilities, and (2) the volume of meaningful CVEs would scale linearly. AI-assisted vulnerability discovery breaks both assumptions simultaneously. Brad describes the Mythos shift bluntly: "It's a persistent elevation of capability that the threat actors have, which is that they can discover vulnerabilities at machine speeds now."

The implication for cloud security leaders is that the yardstick itself has to change. The new metric is the exposure window how long a given vulnerability was actually exploitable inside your environment, taking into account business context, mitigating controls, network reachability, identity blast radius, and attack-chain composition. Patching faster doesn't get you there; some of the most consequential issues this week (PAN-OS, Bleeding Llama) were exploitable for weeks before patches existed.

That reframing forces a series of architectural and operating-model changes and it's the central thread running through this week's news as well as the conversation below.

Featured Experts This Week 🎤

Brad Hibbert — COO & Chief Strategy Officer, Brinqa
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

Claude Mythos: Anthropic's frontier model currently in private testing for security and intelligence applications, including AI-assisted vulnerability discovery at scale. Brad describes it as a "climate change" shift versus prior temporal events like Heartbleed and Log4Shell.
Exposure Window: The duration a vulnerability is actually exploitable in your environment — distinct from the patch window. Driven by reachability, mitigating controls, and identity blast radius, not just CVSS severity.
CTEM (Continuous Threat Exposure Management): Gartner-defined program model that emphasizes continuous discovery, validation, prioritization, and mobilization. Brad argues the Mythos compression effectively forces every program toward CTEM-style operations on a compressed timeline.
EPSS (Exploit Prediction Scoring System): Probability score for whether a CVE will be exploited in the wild within 30 days. Useful as a complement to CVSS, but Brad's caveat lands: "if everything is exploited and everything's kind of ranked the same, how do you provide better guidance to your team?"
Attack Chain Analysis: The practice of evaluating multiple low/medium-severity findings together as a path-to-impact, rather than node-by-node. Mythos demonstrated chained privilege escalation across three medium CVEs to achieve root.
Non-Human Identity (NHI): Service accounts, API keys, OAuth tokens, machine credentials, and AI-agent identities — typically outnumbering human identities 10-to-1 in modern enterprises.

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💡Our Insights from this Practitioner 🔍

1. Heartbleed was a storm. Mythos is climate change.

Brad opens the conversation with a frame that's worth sitting with. Past high-profile vulnerabilities Heartbleed, Log4Shell were temporal. They caused intense activity, then closed out. The exploit cycle had a beginning, a middle, and an end.

"It's not just a temporal thing — it's a persistent elevation of capability that the threat actors have... your months went down to weeks, and in some cases down to seconds before these things can be exploited." — Brad Hibbert

What this changes for cloud security leaders is the planning horizon of the program itself. A program designed to handle 12 Heartbleed-class events per year fails when the underlying capability shift is permanent. The PAN-OS exploitation pattern this week successful RCE within a week of first attempts, log destruction inside the same operation is the normal tempo now, not the anomaly.

What to do with this: Audit your program's design assumptions. If your SLAs, change-control windows, and remediation handoff cadence were built when "manual vulnerability research at machine scale" was a contradiction in terms, those assumptions need an explicit refresh. Brad's framing for the board: "It's not about closing off your criticals in 30 days to meet PCI compliance. It's about how exploitable, what's that exposure window, and how am I showing that go down?"

2. The threat model isn't dead — but the assumptions inside it are

When Ashish asks whether existing threat models are still valid, Brad's answer is nuanced: the structure holds, but the embedded assumptions don't.

"The assumptions that sophisticated attacks required sophisticated attackers has kind of gone away." — Brad Hibbert

Three specific assumptions Brad calls out:

Attacker scarcity. The cost of mounting a sophisticated attack has collapsed. The Mexican government breach earlier this year — where a single actor used Claude to steal 150GB across nine agencies validated this empirically.
Time between discovery and remediation. PCI's 30-day window for criticals is an artifact of an era when 30 days was a reasonable gap before exploitation. It isn't anymore.
CVSS as primary prioritization. Brad's point: when 40,000 highs become 80,000 highs and EPSS marks most of them as likely-exploited, prioritization based on severity scores degenerates into noise.

Practitioner translation: Run a tabletop exercise this quarter where the trigger is "a new CVE-2026-0300-class vulnerability is disclosed at 9am with public PoC by noon, mass exploitation by midnight." Where does your program fail? That's your investment list.

3. The new yardstick is exploitability and the exposure window

This is the core argument of the conversation, and the through-line back to the news.

"The biggest thing today is the biggest short-term thing that CISOs need to do is they need to focus on exploitability and explainability... what we've been talking about with a lot of companies right now is you have to get down to exploitability." — Brad Hibbert

Exploitability, in Brad's framing, isn't just "is there a public PoC?" It's the intersection of the vulnerability with your environment: reachability, mitigating controls (EDR, segmentation, identity guardrails), business context, and the existence of attack paths that chain it with other findings.

The exposure window is the operational consequence: how long was the vulnerability actually exploitable in your environment, end-to-end, until you reduced or eliminated that exploitability — whether by patching, segmenting, killing reachability, or applying a compensating control. Brad's distinction is critical: you don't always need to patch to close the exposure window. You need to make the path inactive.

That distinction maps directly to several stories this week. Bleeding Llama: the patch existed but wasn't flagged as security; the real fix for most enterprises was putting an auth proxy in front and binding Ollama to localhost. PAN-OS: patches don't ship until May 13 but restricting User-ID portal access to internal IPs collapses the exposure window today.

4. Stop ignoring the lows. Start mapping attack chains.

One of the sharpest moments in the conversation comes when Ashish acknowledges what most security teams have been quietly doing for years:

"90% of the time, a lot of the lows were just simply ignored because like, 'Hey, it's a low.' I don't know how many organizations have done this ever." — Ashish Rajan

Brad's response confirms the gap:

"Three medium vulnerabilities, leveraging privilege escalation, could give them root access to a machine versus one standalone critical CVE... you've got to back up and take a look at it not from a node lens, but from a network path lens and attack chain lens." — Brad Hibbert

This is what AI-assisted attackers do natively. Mythos demonstrated the ability to chain three medium CVEs into a privilege escalation that would not have triggered any individual high/critical SLA. Defenders running CVSS-only prioritization will keep missing these because the analytical lens is wrong; they're evaluating findings node-by-node when the attacker is reasoning over graphs.

Practical implication: This is one of the strongest arguments for the kind of unified exposure data plane Brad describes. You cannot compose attack chains across silos. If your CSPM, EDR, vulnerability scanner, and identity tools all run independent AI-driven prioritization, you get siloed AI decisions which are structurally weaker than what an integrated attacker is doing.

5. Remediation has been the forever-problem because the objective was misaligned

Ashish's question "why have we never solved remediation?" opens the most operationally useful section of the conversation. Brad's answer is that the security and remediation teams have been measured on different things:

"If you have two teams that are measured differently — one team's measured on how quickly they can identify and prioritize, the other team's measured on how quick a patch gets released — they're two different measurements. If you focus on the same outcome as a shared objective, which is to reduce the exploitability window, out of that will follow a bunch of other decisions." — Brad Hibbert

This is the kind of strategic-architectural insight senior cloud security leaders can act on without buying anything new. The fix is the operating model, not tooling: rewrite the shared OKR for security + cloud ops + dev so both sides are measured on reduction of exposure window rather than time-to-patch and time-to-detect respectively.

Brad also flags the friction layer that bottoms most programs out — the manual handoff:

"They wanna know why is A ahead of B? Why is B ahead of C? When you pass that information, if you have a shared objective and a shared understanding for how the security team is prioritizing, and an agreed upon approach... you have less of this 'let me export that to Excel, let me compare that to my scanner.'" — Brad Hibbert

Building shared explainability as to why a finding was prioritized, in language remediation teams trust is the trust-building work that lets you eventually automate decisions. Without it, every prioritization becomes a negotiation.

6. Trust is a muscle. Automate incrementally, but start.

When Ashish presses on whether AI-suggested remediation can be trusted enough to automate, Brad's answer is staged but firm:

"You can automate things that are simple, that have very minimal impact, that are reversible. That's great. But if it's not reversible and can have an impact, then you start to get a little queasy in your stomach... they have to build up trust as they start to automate these processes through reasoning and through AI. They will start taking that 5% that they automate today, the 6%, the 10%, the 20%." — Brad Hibbert

The pattern he describes is small, reversible, low-blast-radius first, building toward more consequential decisions with explainability and audit trails throughout — matches what mature platform-engineering teams already do for production change management. The application of the same pattern to security remediation is the bridge that's been missing.

Cloud-security takeaway: Pick one cloud-native control where automation is reversible (e.g., revoking an over-privileged IAM role, blocking egress from a workload, rotating a service-account secret) and instrument it end-to-end with audit logging and rollback. That's your wedge for trust-building. Once the muscle exists, extending to less reversible actions becomes a policy conversation, not a technical one.

7. Don't boil the ocean pick one high-stakes asset and prove the model

When asked about quick wins, Brad is pragmatic:

"If you're gonna build on the top of the pyramid, don't try to do everything across the whole asset stack. Focus on a high-profile application that could have significant impact to the business, and maybe focus on your external attack surface first. Pick your poison... work the kinks out, work on that shared responsibility, kind of what the shared measurements are gonna be. Show the model working and then expand from there." — Brad Hibbert

This is the most actionable 30-60 day playbook from the conversation:

Pick one high-business-impact application or one critical external attack surface segment.
Define a shared exposure-window OKR across security and the relevant remediation team.
Instrument the full lifecycle — discovery, enrichment, exploitability assessment, prescriptive remediation guidance, two-way ticketing integration, post-remediation verification.
Measure exposure-window reduction, not patch volume.
Use the working model as the proof case to expand scope.

8. The convergence problem: siloed AI is going to fail

Brad's closing observation is one cloud security leaders should plan for now:

"I think a lot of security vendors are gonna say, 'We have the solution, it's AI.' But then what you're gonna do is you have these siloed security products making siloed AI decisions. What organizations need is to bring all this information into a global exposure repository so they can understand everything from a global perspective." — Brad Hibbert

The Cisco/Astrix deal in this week's news is, in part, a bet on this convergence pulling NHI, identity intelligence, secure access, and SIEM (via Splunk) into a single context-aware control plane. Expect more consolidation along the same line over the next 18 months. For practitioners building their own roadmap, the design principle is to invest in data integration and unified context before adding more siloed AI features.

Brinqa — Resources on Mythos and Exposure Management
CISA Known Exploited Vulnerabilities Catalog
CISA CI Fortify Initiative — guidance for critical infrastructure operators on isolation and recovery
Unit 42 PAN-OS Threat Brief (CVE-2026-0300)
Microsoft Security Blog — AiTM Code-of-Conduct Campaign Analysis
Cyera Research — Bleeding Llama Disclosure
Gartner CTEM Framework Overview — for teams building toward continuous threat exposure management

Podcast Episode

Cloud Security Podcast -Full Episode with Brad Hibbert- Complete transcript and audio for this week's featured conversation

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🤔 If you measured your program on exposure window reduction instead of patch SLA next quarter, which OKR breaks first — and what does that tell you?

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An AI gateway exploited in 36 hours

Ashish Rajan — Wed, 29 Apr 2026 21:00:00 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: What's Missing From Most AI Security Programs (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

Two stories defined this week, and both expose the same gap. On the AI side, the LiteLLM SQL injection (CVE-2026-42208) was exploited in the wild within 36 hours of disclosure, the AI gateway turned out to be a credential vault holding OpenAI, Anthropic, and AWS Bedrock keys in a single PostgreSQL row. On the platform side, Wiz disclosed a GitHub RCE (CVE-2026-3854) reachable via a single git push, with cross-tenant blast radius on shared storage. Different bug classes, same underlying signal: the security perimeter for cloud workloads has moved up the stack into AI gateways, agent identities, and the platforms between developers and production, and most enterprise AI security programs were scoped before any of this was on the map.

To unpack the gap and what to do about it, we sat down with Shawn Hays, Product Marketing Manager for Microsoft Applications and AI Security Solutions at Varonis. Shawn spent six years configuring CMMC environments for defense industrial base customers, three years inside Microsoft on the Purview/Defender/Sentinel go-to-market, and is now driving Varonis's AI security platform, Atlas. His central argument, that we've entered a "multi-AI era" analogous to the multi-cloud explosion of fifteen years ago, and that the market has over-pivoted on AISPM while leaving guardrails, pen-testing, and runtime enforcement underbuilt, is the lens this newsletter uses to read the news. [Listen to the episode]

⚡ TL;DR for Busy Readers

This week’s attacks didn’t break systems — they used them


🔑 AI gateways are Tier-0 secrets stores. LiteLLM's litellm_credentials table holds enterprise cloud provider keys, treat every AI proxy as you would your IAM root, and rotate now if you ran a vulnerable build.  

🧬 "Agent identity" just became a procurement category. Google Cloud Next '26 introduced Agent Identity, Agent Gateway, and Model Armor primitives, IAM roadmaps without scoped non-human identity will fall behind in 2026.  

🏗️ AISPM alone is not an AI security program. Shawn Hays argues the market has over-pivoted on posture and visibility while leaving guardrails, pen-testing, and runtime monitoring underbuilt, close the gap before regulators do.  

🔗 Vendor-environment access is the breach pattern of the quarter. Anthropic Mythos, Citizens/Frost, and the Anthropic contractor incident all share the same root cause, third-party identity hygiene that doesn't match the sensitivity of what those vendors can reach. 

🛠️ Edge persistence outlasts patching. FIRESTARTER on Cisco firewalls and the April 24 KEV additions (SimpleHelp, Samsung MagicINFO, D-Link) prove that "we patched, so we're clean" is no longer a defensible posture for hybrid cloud environments.

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

1. LiteLLM SQL Injection (CVE-2026-42208) Exploited Within 36 Hours: AI Gateway Becomes Cloud-Account-Class Risk

What Happened. A pre-authentication SQL injection in BerriAI's LiteLLM (CVSS 9.3) was indexed in the GitHub Advisory Database on April 24 and saw its first observed exploitation attempt on April 26 at 16:17 UTC, roughly 36 hours later. The flaw concatenates the Authorization Bearer value into a query without parameterization, letting unauthenticated attackers run arbitrary SQL against the PostgreSQL backend. Sysdig observed targeted UNION-based payloads from German-hosted IPs (AS200373) hitting precisely the three highest-value tables: LiteLLM_VerificationToken (virtual API keys + master key), litellm_credentials (stored OpenAI/Anthropic/Bedrock provider credentials), and litellm_config (environment variables). Affected versions: 1.81.16 through 1.83.6. Fixed in 1.83.7-stable.

Why It Matters. LiteLLM has 45,000+ GitHub stars and is widely deployed as the AI gateway in front of multi-provider LLM architectures. A single litellm_credentials row can hold an OpenAI org key with five-figure monthly spend, an Anthropic console key with workspace admin rights, and an AWS Bedrock IAM credential, meaning the blast radius is closer to a cloud account compromise than a typical web SQLi. Three takeaways: (1) inventory every AI gateway, proxy, and middleware tier and treat them as Tier-0 secrets stores, not developer convenience tooling; (2) any internet-facing LiteLLM instance running a vulnerable version during the exposure window should be assumed compromised; rotate every key and audit upstream provider billing; (3) the operator-grade exploitation (Prisma schema awareness, schema-aware column-count enumeration) means GHSA-only critical advisories now warrant KEV-level urgency.

🔎 Sources: Sysdig analysis

2. GitHub RCE via Single Git Push (CVE-2026-3854): Wiz Discloses Cross-Tenant Risk

What Happened. On April 28, GitHub and Wiz coordinated disclosure on CVE-2026-3854, a CVSS 8.7 command injection in GitHub's internal git push pipeline. By chaining three injections through unsanitized push option values, an authenticated user with push access could override the rails environment, redirect the custom hooks directory, and trigger path traversal via repo_pre_receive_hooks to execute arbitrary commands as the git user (with cross-tenant blast radius on shared storage). GitHub.com was patched within two hours of Wiz's report; per CISO Alexis Wales, ~88% of GHES instances were vulnerable at disclosure. The bug was discovered using AI-assisted reverse engineering (IDA MCP).

Why It Matters. Two threads to track. Operationally, any GitHub Enterprise Server instance must be on 3.19.3 or later. Wiz called the exploit "remarkably easy." Architecturally, the lesson is that when multiple services in different languages pass data through a shared internal protocol, the assumptions each service makes about that data become a critical attack surface. It's the same pattern that has haunted ingress-nginx and other shared-data systems. This is also the third notable GitHub incident in a single week (alongside the merge queue regression of April 22–23 and an April 27 search outage), which is putting platform-dependency assumptions under stress for compliance teams.

🔎 Sources: Wiz Research

3. Google Cloud Next '26: Wiz Integration Goes Deep, Agentic Defense Lands

What Happened. Google Cloud Next '26 ran April 22 in Las Vegas with a security agenda that, post-Wiz acquisition, finally looked unified. Headline announcements: three new Google Security Operations agents (Threat Hunting, Detection Engineering, Third-Party Context); Wiz Defend detections natively forwarded to Google SecOps and Mandiant Threat Defense; expanded Wiz coverage to Databricks, AWS AgentCore, Azure Copilot Studio, Salesforce Agentforce, Cloudflare AI Security for Apps, and Vercel; agent-governance primitives (Agent Identity, Agent Gateway, Model Armor integration); reCAPTCHA reborn as Google Cloud Fraud Defense; and KMS Quantum Safe Key Imports in preview. Google's M-Trends 2026 data claims initial-access-to-handoff time has collapsed from 8 hours three years ago to 22 seconds today.

Why It Matters. Two structural takeaways. First, "agent identity" has moved from concept to procurement category: Agent Identity, Agent Gateway, and Model Armor sketch the primitives every enterprise will need as autonomous agents proliferate inside production. Second, the Wiz/Google SecOps integration is meaningful but more incremental than secondary coverage suggests. Google's own language is careful ("updated how we integrate") rather than fully native. Custom parsing, normalization, and SOAR content sitting between Wiz and Chronicle UDM today is not automatically obsolete. CISOs should ask vendors specifically what changes versus what's marketing gloss. Third, the SCC Standard tier now bundles posture, compliance, and vulnerability management; worth a hard look for teams paying separately today.

🔎 Sources: Google Cloud Blog

4. ServiceNow Closes $7.75B Armis Acquisition, Reshaping Asset-Centric Security

What Happened. ServiceNow completed its all-cash $7.75 billion acquisition of cyber exposure management vendor Armis on April 20, six months ahead of the originally guided H2 2026 close. Together with the pending Veza identity acquisition, ServiceNow says the combination will more than triple its addressable market for security and risk solutions, embedding real-time asset discovery across IT, OT, IoT, medical devices, "physical AI," and cloud directly into the ServiceNow platform.

Why It Matters. This is a structural bet that the next decade of enterprise security runs through asset-and-identity context, not more detection. For cloud security leaders: (1) ITSM-native CMDBs are about to absorb cyber asset intelligence, which will pressure standalone CAASM tooling and reshape how exposures get prioritized; (2) the OT/IoT/medical visibility coming with Armis pulls non-IT assets into the same pane of glass as cloud workloads (meaningful for healthcare, manufacturing, and CNI buyers running hybrid estates); (3) for CISOs running ServiceNow as the system of record, the integration roadmap is now the ceiling on how fast you can collapse asset, vulnerability, and exposure tools. Plan for a 12–18 month integration window before depth catches up to the marketing.

🔎 Sources: ServiceNow Newsroom

5. Anthropic Investigates Unauthorized Access to "Mythos" Cyber Model via Vendor Environment

What Happened. Bloomberg reported on April 21 that a small Discord group of AI enthusiasts gained unauthorized access to Anthropic's Claude Mythos Preview, the vulnerability-discovery model restricted to Project Glasswing partners (Apple, Microsoft, Cisco, Amazon, Mozilla, several major banks, and reportedly the NSA). The group leveraged credentials from a third-party Anthropic contractor and guessed the model's endpoint URL based on naming-convention knowledge, gaining access on April 7, the same day Glasswing was publicly announced. Anthropic confirmed the investigation and characterized the access as scoped to a third-party vendor environment.

Why It Matters. Strip away the AI framing and this is a textbook contractor-credential-meets-predictable-naming-convention failure. Lessons: (1) third-party vendor environments holding access to your most sensitive systems need the same identity rigor as your own production: scoped credentials, short-lived tokens, no shared environments; (2) predictable naming conventions for staging, preview, and unreleased resources are an under-appreciated reconnaissance surface; (3) controlled-distribution governance for dual-use AI capability will keep failing in similar ways unless the access control layer matches the model's sensitivity. With OpenAI's GPT-5.4-Cyber and Google's Big Sleep operating in similar territory, expect more of these incidents, and expect frontier-AI access controls to become a board-level question for any organization participating in these partner programs.

🔎 Sources: TechCrunch

6. CISA & NCSC: FIRESTARTER Implant Survives Patches on Cisco Firewalls

What Happened. On April 23, CISA and the UK NCSC published a joint malware analysis report on FIRESTARTER, a custom Linux ELF backdoor found on a U.S. federal civilian agency's Cisco Firepower device running ASA software. Tracked to UAT-4356 (the same cluster behind ArcaneDoor), the implant was deployed in September 2025 via CVE-2025-20333 and CVE-2025-20362, and crucially persisted through the patches the agency later applied. CISA updated Emergency Directive 25-03 the same day, requiring federal agencies to collect device core dumps. Cisco recommends full reimaging; only a hard power cycle clears the persistence mechanism.

Why It Matters. "We patched, so we're clean" no longer holds for any organization that ran an internet-exposed ASA between September 2025 and patching. FIRESTARTER hooks into LINA, modifies the boot file, and re-launches itself on signal. For hybrid cloud architects specifically, these devices typically terminate site-to-site VPNs into AWS/Azure/GCP and house the credentials, certificates, and routing trust that connect on-prem to cloud workloads. A compromised firewall is also a compromised cloud egress path. Concrete actions: (1) treat any device exposed during the September 2025 window as compromised regardless of patch state; (2) plan reimaging, not patching, and rotate every credential, certificate, and key that touched the box, including cloud-side IAM roles or service account credentials accessible from those tunnels.

🔎 Sources: CISA AR26-113A

🎯 Cloud Security Topic of the Week:

What's Missing From Most AI Security Programs

The dominant question Shawn Hays hears at RSA from CISOs is some version of "I bought one AI security tool, why doesn't it cover my whole estate?" His answer is uncomfortable: most enterprises are already in the multi-AI era, and most AI security purchases were made for a single-vendor world that no longer exists. Copilot was the beta. Then Copilot Studio agents. Then Atlassian shipped Jira agents. Then Salesforce Agentforce. Then a business unit picked Bedrock for a specific use case, another picked Foundry for another, and somewhere a developer wired in an MCP server pointing at a Hugging Face model. Now the AISPM tool that scopes only to Microsoft prompts and responses sees a fraction of the surface.

Shawn frames this as a direct parallel to the multi-cloud transition fifteen years ago: every booth at RSA 2010 was selling multi-cloud security because organizations had lifted-and-shifted to "this place and that place and that place" without knowing how to protect it. We are now living the same pattern with AI, and the program design that worked for a single-stack AI strategy is not going to carry forward. But the bigger gap, in Shawn's view, is not breadth. It's depth. Most programs have visibility and posture management, and almost nothing else. The rest of this newsletter walks through the eight pillars he uses to frame an enterprise-grade AI security program, and why AISPM and visibility alone are the wrong place to stop.

Featured Experts This Week 🎤

Shawn Hays ex-Product Marketing Manager for Microsoft Applications & AI Security Solutions at Varonis.
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

AISPM (AI Security Posture Management). Continuous discovery, posture assessment, and risk prioritization across AI components such as models, agents, MCP servers, code repos, and datasets. Analogous to CSPM but for AI estates.
DSPM (Data Security Posture Management). Continuous discovery and risk assessment of sensitive data across cloud and SaaS, including who has access and how it's classified.
ITDR (Identity Threat Detection and Response). Behavioral monitoring of identities (both human and non-human) for compromise indicators like privilege escalation, anomalous logins, and lateral movement.
CIEM (Cloud Infrastructure Entitlement Management). Visibility and right-sizing of permissions for identities accessing cloud resources.
AI Bill of Materials (AI BoM). A manifest of components inside an AI system (models, datasets, MCP servers, prompts, tools, dependencies). Analogous to SBOM for software supply chain.
MCP (Model Context Protocol). The emerging standard for how agents call external tools and data sources. An MCP server exposes capabilities (e.g., "read this database," "call this API") that an agent can invoke at runtime.
RAG AI (Retrieval-Augmented Generation). AI systems like Copilot that ground responses in data the prompting user already has access to, via token-exchange checks at query time. Permissions are inherited from the user.
Guardrails. Runtime controls that block an agent from taking specific actions or producing specific outputs. Input guardrails (e.g., "reject prompt-injection attempts"), output guardrails (e.g., "never emit PHI").

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💡Our Insights from this Practitioner 🔍

1. The "Multi-AI Era": Why It Changes Program Design

Shawn opens with a frame that anyone who lived through 2010-era multi-cloud chaos will recognize immediately:

"I think we are now entering this multi-AI era where no longer is an organization, an enterprise, sophisticated organization, just using copilot. They're using all these different pieces." Shawn Hays, Varonis

The implication for security architecture is direct. A program that scopes only to Microsoft Copilot's prompts and responses will not see Jira agents, Agentforce, Bedrock pro-code agents, or MCP servers pulling in third-party models. Shawn describes a recurring conversation with enterprises who bought a single-vendor AI security tool early, and then discovered, as they matured, that "the entirety of the AI that they have, or maybe the AI they're going to have" exceeds what one tool can cover. This is the AISPM equivalent of early CSPM tools that only saw AWS: useful, but incomplete the moment a second cloud showed up.

What to do about it. When evaluating an AI security platform, Shawn's recommended buyer's question is: "Can it protect all the AI I've built today, all the AI I plan to build tomorrow, and all the AI I don't even know about?" If the answer scopes to a single hyperscaler or a single AI vendor, the tool is solving a 2024 problem.

2. The Eight Pillars: AISPM Is Necessary, Not Sufficient

Shawn argues the market has over-pivoted on AISPM and visibility. It's the same pattern Varonis saw in DSPM five years ago, where customers bought posture tools and then realized they had no enforcement layer. The full program he describes covers eight areas; the pillars he emphasizes most:

Inventory and observability across every layer of the AI stack, models, MCP servers, agents, services, code repos, even Jupyter Notebooks (where he's seen developers stash secrets for convenience). Continuous, not point-in-time.
AISPM, misconfigurations, vulnerabilities, posture drift across that inventory.
AI Bill of Materials for both internal-built systems and third-party AI services. If a model has a CVE in NIST's NVD, you need to know it's in your stack, and you need to know it's in Grammarly's stack too.
Pen-testing of agents before they go live. As Shawn puts it, you "need to put it through the ringer… both from a jailbreaking, poisoning [perspective] but also very run-of-the-mill interactions to see how it's gonna behave."
Runtime guardrails that block specific behaviors, input guardrails for prompt injection, output guardrails for sensitive-data emission.
Compliance monitoring mapped to frameworks like NIST AI RMF that re-evaluate as the agent changes.
Third-party AI risk management including AI BoM ingestion from vendors.
Continuous monitoring across the full lifecycle, not just deployment.

The strategic insight underneath this list:

"They have great visibility, they have inventory, they know every piece of their AI system, but they really have no way of preventing that agent or AI system from going off the rails." - Shawn Hays, Varonis

This is the most actionable critique in the conversation. Many enterprises in 2026 will pass an internal audit of their AI security program, they have the dashboards, they have the inventory, they have CVE alerting on models, and still have nothing in front of an agent that would stop it from doing something stupid in production. The pen-testing-and-guardrails layer is where most programs are thinnest.

3. Identity Is Woven Through Everything: Why Native Tools Won't Carry You

Shawn is precise about how identity sprawls across an agentic estate:

"You've got identities for the folks that can access data in the cloud store… you have the identity of the agent, you have identity of the builders, like the people making these agents… if they're using some sort of Bitbucket, GitHub, you have identities for those that can have access to the code repos. It's like there's an identity layer woven throughout." - Shawn Hays, Varonis

Each of those identity surfaces needs ITDR coverage. Not just the cloud architect (with normal CIEM/ITDR for elevated privileges and lateral movement), but the agent identity itself, alerting on agents that suddenly gain entitlements, access resources they typically don't, or "feverishly" light up after a period of dormancy.

Ashish pushes on the obvious counter, "I have an E5 license, the native services cover this, right?", and Shawn's response is the most quotable piece of practical guidance in the episode:

"Native tools are really good about solving some of the native challenges. But then once you start broadening the scope and the aperture, that's when it gets a little tough." - Shawn Hays, Varonis

The example he uses is HIPAA. Microsoft Purview will do an excellent job preventing PHI exfiltration via labeled data and DLP policies, inside the Microsoft tenant. The moment an AWS Bedrock agent calls an EHR system through an MCP server, that protection envelope ends, but the regulator's expectation does not. For CISOs in regulated industries, this is the architectural argument for a cross-stack AI security platform regardless of how aligned your primary cloud is.

4. The Connector Ecosystem Is Quiet Third-Party Risk

The connector ecosystem is the part of the AI security problem most enterprises haven't budgeted for. Shawn's example: turning on the Salesforce connector in Microsoft 365 Copilot doesn't require deploying a "highly configured, sophisticated pro-code AI solution." It's a checkbox. But the moment that connector is on, Copilot is grounded in Salesforce data via the same can-access model, and any data permission misconfiguration in Salesforce now flows into Copilot output.

This is why DSPM and AI security are intertwined, not adjacent. RAG AI inherits user permissions; if those permissions are over-permissive, the AI is over-permissive. Shawn's guidance for Copilot governance specifically (and it transfers to any RAG-based AI tool in your estate):

Understand what data Copilot can access.
Test whether existing classification and labeling actually works.
Define how new data will be classified and labeled going forward.
Apply zero trust at runtime: monitor how Copilot interacts with data even after permissions are right-sized.

5. Zero Trust Applied to the Whole Chain, Not Just the User

The strongest architectural insight in the conversation is Shawn's reframing of zero trust as applied to every actor in an agentic transaction simultaneously:

"I need to not trust that agent. I need to even not trust the user prompting… I need to also not trust the cloud architect that's over that maybe data SQL database that's sitting in Azure. I wanna apply zero trust to that entire chain, and the reason being [is] data." - Shawn Hays, Varonis

In a healthcare patient-facing agent example, this means: don't trust the prompt (it might contain a jailbreak embedded inside legitimately-ingested PHI), don't trust the agent's downstream actions (it might write PHI to the wrong table), don't trust the Azure architect's identity (it might be compromised), and don't trust the cloud configuration (misconfig could leak data via SQL). Each link is a separate enforcement point with separate controls.

This is also the lens that makes this week's news cohere. The Anthropic Mythos incident violated trust at the contractor link. FIRESTARTER violated trust at the network appliance link. LiteLLM violated trust at the AI gateway link. None of these were AI-specific in the bug-class sense. They were identity-and-access failures dressed up in different costumes.

The "Ron Burgundy" Analogy: Why Data and Identity Will Outlast Every AI Architecture

When Ashish asks what controls will stand the test of time as 2027 and 2028 AI architectures arrive, Shawn lands on an analogy worth keeping:

"At least the AI we have now, and maybe for the next five years… AI is Ron Burgundy. It's only gonna read what's on the teleprompter. So if we're looking at identity solutions and data security… it's like, how are we gonna make sure that the right data shows up on the teleprompter?" - Shawn Hays, Varonis

The point is durable: whatever the next-generation agent architecture looks like, it will still be reading from a context window, and that context window is still being populated by data systems and identity decisions that you control. Right-size data access, instrument identity at every layer, and apply zero trust to the chain. Do that, and you'll be in a defensible position regardless of what AI architecture wins next.

Ashish's Frame: The Horse Has Left the Barn

Ashish makes the operational counterpoint that should sit with every CISO reading this:

"With AI, that horse has left the barn." - Ashish Rajan, Cloud Security Podcast

Data classification programs that "never had the rubber hit the road" (Ashish's words from his own CISO experience) are no longer a deferrable problem. The assumption that confidential data stays inside organizational boundaries is broken the moment an agent reaches into Salesforce, Jira, or a third-party MCP server. The teams that get ahead in 2026 are the ones treating data classification, identity hygiene, and access right-sizing as the AI security work, because, per Shawn's argument, that is the AI security work for the dominant RAG-based AI patterns.

Practical Application: A 30–60 Minute Action List

Drawing from Shawn's eight pillars and this week's news, the immediate work for cloud security teams:

Inventory every AI gateway, proxy, and middleware in your estate (LiteLLM, Portkey, custom OpenAI proxies, etc.) and treat their secrets stores as Tier-0. If you ran a vulnerable LiteLLM build, rotate now.
Map your full multi-AI footprint (Copilot, Copilot Studio, Foundry, Bedrock, Agentforce, Jira agents, custom agents, MCP servers, and any third-party SaaS using AI as a feature). Score each for AI BoM availability.
Apply ITDR to non-human identities (agents and service principals), not just to humans. Anomalous agent behavior should page the SOC the same way anomalous human behavior does.
Audit your connector ecosystem. Every cross-product connector (Copilot ↔ Salesforce, etc.) inherits permissions. Run a DSPM/AISPM pass on the data side of each connector.
Add pen-testing and guardrails to your agent SDLC. If your AI program documentation only describes posture and inventory, it's incomplete.

Varonis Atlas overview: Varonis's AI security platform
Varonis blog: Applying Zero Trust to MCP Servers: Shawn's recent write-up referenced in the conversation
Sysdig: CVE-2026-42208 LiteLLM Analysis: exploitation timeline and detection guidance
Wiz Research: GitHub CVE-2026-3854: full technical disclosure
CISA Emergency Directive 25-03 (FIRESTARTER): federal guidance applicable to all enterprises running ASA/FTD
NIST AI Risk Management Framework (AI RMF): compliance reference for AI security programs
Microsoft Purview + Copilot governance documentation: native controls baseline for the Microsoft stack
Google Cloud Next '26 security keynote recap: Agent Identity, Agent Gateway, Model Armor announcements

Podcast Episode

Cloud Security Podcast -Full Episode with Shawn Hays- Complete transcript and audio for this week's featured conversation

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🚨 Vercel OAuth Attack | How AI Is Breaking Cloud Security (What CISOs Must Do Now)

Ashish Rajan — Wed, 22 Apr 2026 22:38:26 +0000

Hello from the Cloud-verse!

This week’s Cloud Security Newsletter topic: Agentic Cloud Security: Why the CNAPP Must Evolve Before Your Adversaries Do (continue reading)

This image was generated by AI. It's still experimental, so it might not be a perfect match!

Welcome to this week’s Cloud Security Newsletter

This week’s uncomfortable truth:

Attackers are no longer breaking into your systems.
They are operating inside them using your tools, your APIs, and your trust relationships.

APT41 is stealing IAM credentials using cloud metadata APIs
Vercel was breached without a vulnerability - just OAuth trust abuse
Microsoft Teams is being used to impersonate IT helpdesks
Cisco ISE can now be taken over with read-only credentials

And according to Palo Alto Networks Research: It’s taking ~25 minutes from breach to data exfiltration. [Listen to the episode]

⚡ TL;DR for Busy Readers

This week’s attacks didn’t break systems — they used them


🔴 APT41 cloud credential theft: 
Winnti backdoor harvesting AWS/Azure/GCP tokens via SMTP (zero detections). Block outbound SMTP from non-mail workloads NOW  

🔑 SaaS tokens = your weakest link: 
Vercel breached via over-permissioned OAuth — API keys, GitHub & NPM tokens exposed. Audit third-party OAuth access TODAY  

⚠️ Identity isn’t safe: 
Cisco ISE CVSS 9.9 flaws exploitable with read-only credentials. Patch manually — Cisco can’t do this for you  

📦 Third Party Breach risk is live ( 1yr later): 
A 2025 Salesforce compromise is still exposing new victims — including 13.5M user records and SSNs — nearly a year later.  

🤖 CNAPP model is breaking: 
25-minute breach-to-exfiltration window confirmed — human-speed response is no longer viable

📰 THIS WEEK'S TOP SECURITY HEADLINES

Each story includes why it matters and what to do next — no vendor fluff.

🚨 1. Microsoft Teams Used for Helpdesk Impersonation Attacks

What’s happening

Microsoft has documented a nine-stage attack chain where Attackers are spinning up fake Microsoft tenants and impersonating internal IT via Teams → convincing employees to start remote sessions → then moving laterally and exfiltrating data.

Why this matters

No malware. No exploit.
Just trusted tools used against you.

Your EDR sees normal activity.
Your users see “IT support”.

This is a living-off-the-land attack that requires no malware, no CVEs, and no phishing emails. It exploits the trust users have placed in a familiar collaboration platform. Because the attacker operates from a Microsoft-issued tenant using Microsoft-sanctioned tools, most endpoint detection stacks will see Zoom-like remote access activity with no signal that anything is wrong. The cross-tenant access feature is enabled by default in most Microsoft 365 deployments and has almost certainly never been reviewed in your environment.

👉 What to do

▶ Audit Teams external access policy immediately. Most organizations have never restricted cross-tenant chat. Limit it to approved domains, or disable it if not operationally required.

▶ Establish out-of-band helpdesk verification. Create a verbal authentication phrase that all IT staff use before initiating remote sessions. Include this in your security awareness training.

▶ Hunt for Rclone in your environment. Rclone has no legitimate enterprise use in most organizations. Its presence on an endpoint is an incident indicator.

▶ Create a detection rule for Quick Assist sessions from external tenants. This pattern is unusual enough that a well-scoped rule should have near-zero false positives.

Sources: BleepingComputer | CSO Online

🚨 2. Salesforce Breach Surfaces Downstream: McGraw-Hill 13.5M, OneDigital 28K SSNs

What happened:

A 2025 Salesforce compromise is still exposing new victims — including 13.5M user records and SSNs — nearly a year later. OneDigital confirmed approximately 28,414 individuals had names and SSNs exposed. McGraw-Hill disclosed that ShinyHunters stole and publicly leaked 13.5 million user accounts via the same underlying Salesforce breach. The gap between the original compromise and these disclosures is approaching twelve months for some affected individuals.

Why it matters:

The Salesforce breach itself is not the primary lesson here. The lesson is the blast radius and the disclosure lag

👉 What to do

▶ Treat CRM as tier-1 cloud infrastructure. Apply the same access controls, anomaly detection, and logging posture to Salesforce that you apply to your data warehouse.

▶ Renegotiate SaaS breach notification SLAs. If your contracts do not specify a notification timeline for platform-level incidents, you have no contractual floor.

▶ Backdate your investigation window. When a downstream notification arrives, treat the compromise date not the notification date as your start point.

Sources: BleepingComputer | Palo Alto Unit 42 | Cybersecurity Dive | Rapid7

☁️ 3. ⚡ APT41 Deploys Zero-Detection Cloud Credential Backdoor to Harvest Credentials Across AWS, Azure, GCP

What happened:

APT41 is harvesting credentials from AWS, Azure, and GCP using metadata APIs and hiding traffic in SMTP.

Zero detections at time of discovery.

Why it matters:
This is the most cloud-native credential theft operation we have seen from a state-sponsored actor. The backdoor does not exploit a vulnerability in your cloud environment it queries the same metadata APIs your applications use legitimately.

On AWS it hits the IMDS endpoint at 169.254.169.254 for IAM role credentials. On Azure it pulls managed identity tokens. On GCP it requests service account tokens. It also sends periodic UDP broadcast beacons on port 6006 for peer-to-peer lateral coordination between compromised hosts, meaning the C2 infrastructure can go dark while the campaign continues to propagate internally.

This attack:

Uses legitimate APIs
Avoids HTTP/DNS monitoring
Blends into normal cloud behaviour

This bypasses:

EDR
Signature detection
Traditional network monitoring

This is a cloud-native attack on your control plane

👉 What to do

▶ Block or alert on outbound SMTP (port 25) from non-mail workloads. This is anomalous in cloud compute environments and should have very low false positives.

▶ Alert on unusual reads of cloud credential files from non-SDK processes: ~/.aws/credentials, ~/.azure/ profile directories, GCP application default credential paths.

▶ Enforce IMDSv2 on AWS and equivalent IMDS hardening on Azure/GCP. This limits the blast radius of a compromised instance by requiring session-oriented token requests.

▶ Hunt for stripped, statically linked ELF binaries in /tmp and /var/tmp. These are not characteristic of legitimate cloud workloads.

▶ Alert on UDP broadcast traffic to port 6006 from compute instances. This is the lateral movement beacon used by this implant.

Sources: Dark Reading | SC Media | CybersecurityNews

🏥 4. Cisco Patches Four Critical Flaws in Webex and ISE - Including Unauthenticated SSO Impersonation

What happened:

Cisco released patches for 15 vulnerabilities including four critical-severity flaws in Webex Services and Identity Services Engine.

CVE-2026-20184 (CVSS 9.8) in Webex allows an unauthenticated remote attacker to impersonate any user by exploiting improper certificate validation in the SSO integration with Control Hub. Three critical ISE flaws (CVE-2026-20147, CVE-2026-20180, CVE-2026-20186, all CVSS 9.9) enable remote code execution on the underlying OS critically, CVE-2026-20180 and CVE-2026-20186 are exploitable with nothing more than read-only administrative credentials. No active exploitation has been confirmed at time of disclosure.

Why it matters:
The Webex SSO flaw has a manual remediation step that Cisco cannot complete on your behalf: admins must upload a new IdP SAML certificate to Webex Control Hub. In enterprises where Webex administration is delegated or outsourced, this step is highly likely to be missed. The ISE vulnerabilities carry a more severe operational implication: ISE underpins 802.1X authentication, NAC, and device trust for many large enterprises. An attacker with a compromised read-only monitoring account a very common post-breach scenario can achieve root code execution on your network access control infrastructure. CrowdStrike’s 2026 Global Threat Report notes that valid account abuse accounted for 35% of cloud incidents last year. These flaws make that even more dangerous.a

👉 What to do

▶ Upload the new IdP SAML certificate to Webex Control Hub NOW if SSO is in use. This is your action item, not Cisco’s.

▶ Patch ISE to fixed releases: 3.1 P11 · 3.2 P10 · 3.3 P11 · 3.4 P6 · 3.5 P3. There are no workarounds.

▶ Audit read-only admin account activity in ISE. Any anomalous activity on these accounts should be treated as a high-priority incident given the low privilege bar for exploitation.

Sources: SecurityWeek | CSO Online | Cisco Security Advisories

5 — Vercel Breach via OAuth Supply Chain Attack

What happened:

On April 19, Vercel disclosed a security breach that began in February 2026 when a Context.ai employee’s machine was infected with Lumma Stealer malware after downloading a Roblox game exploit. The malware harvested Google Workspace credentials and OAuth tokens, which the attacker used to pivot through Context.ai’s AWS environment into a Vercel employee’s Google Workspace account gaining access to Vercel’s internal systems and non-sensitive environment variables. A threat actor claiming ShinyHunters affiliation listed the stolen data for $2M on BreachForums, claiming the haul includes API keys, NPM tokens, GitHub tokens, and 580 employee records. Vercel confirmed the incident, published IOCs, and advised all customers to rotate environment variable credentials.

Why it matters:
This breach chain required zero direct vulnerabilities in Vercel’s own code. The attack path was: infostealer → OAuth token theft → SaaS lateral movement → PaaS credential exposure. Every step exploited legitimate trust relationships between sanctioned enterprise applications. The structural problem is that a single developer using a third-party AI tool with overly permissive Google Workspace OAuth grants became the entry point for an incident that potentially affects hundreds of organizations.

👉 What to do

▶ Audit all third-party OAuth authorizations in Google Workspace and Microsoft 365. Remove any app granted broad read/write access that is not formally inventoried and approved.

▶ Add PaaS deployment platforms to your SBOM and TPRM register. Vercel, Netlify, Railway, Render these are tier-1 supply chain dependencies, not external services.

▶ Rotate all Vercel environment variables not marked as ‘sensitive’. Even without a direct notification, the exposure window spans February–April 2026.

▶ Block or alert on ‘Allow All’ OAuth grants during enterprise onboarding of AI tools. This single permission pattern is the root cause of this incident.

Sources: TechCrunch | BleepingComputer | Help Net Security | Trend Micro Research | Vercel Security Bulletin

🎯 Cloud Security Topic of the Week:

Agentic Cloud Security: Why the CNAPP Must Evolve Before Your Adversaries Do

For most of the past decade, the cloud security conversation was structured around posture. Know your misconfigurations. Remediate your public S3 buckets. Track your IAM sprawl. The CSPM era gave security teams visibility, and visibility was genuinely the right place to start. But a posture score does not stop an APT41 backdoor that is already running on your Linux workload and querying your metadata API. And a misconfiguration dashboard does not help you when an attacker goes from initial access to data exfiltration in 25 minutes.

Elad Koren’s framing in this week’s episode is the clearest articulation of this shift we’ve heard: cloud security has moved from “manage your hygiene” to “protect in real time while maintaining hygiene.” The CNAPP of 2026 is not just a visibility platform. It is an autonomous response layer that can make and execute decisions faster than any human analyst can triage a ticket.

The three structural changes Koren identified as driving this shift are worth examining individually, because each one has a direct implication for how you build or upgrade your cloud security program:

AI is available to adversaries. Attacks that previously required days of reconnaissance and manual exploitation can now be generated and launched with a prompt. Palo Alto’s telemetry shows a 25-minute window from initial access to data exfiltration in active incidents. You cannot staff a human response team capable of operating inside that window.
Vibe coding has removed the development friction that security relied on. When the cycle from “ideation to production” collapses to three days including testing the traditional shift-left security model breaks. There is no left to shift to. Security must be embedded as a continuous automated layer across the entire pipeline, not a review gate before deployment.
AI workloads in cloud represent a posture gap most teams cannot close manually. Organizations are deploying experimental AI applications faster than their security teams can inventory, analyze, and control them. The incident Koren described an internal AI workload accidentally exposed to the internet because the developer had no security context is not an edge case. It is a pattern.
Featured Experts This Week 🎤
Elad Koren - VP, Product Management, Cortex Cloud, Palo Alto Networks
Ashish Rajan - CISO | Co-Host AI Security Podcast , Host of Cloud Security Podcast

Definitions and Core Concepts 📚

Before diving into our insights, let's clarify some key terms:

CNAPP (Cloud-Native Application Protection Platform)
A unified security platform that combines CSPM (posture), CWPP (workload protection), CIEM (entitlements and identity), and increasingly runtime protection and AI workload security into a single data-integrated platform. Elad Koren’s argument is that the CNAPP of 2026 must move beyond posture management into active agentic defense where AI agents can automatically remediate tier-one issues while surfacing complex attack paths for human analysts.
Vibe Coding
A term describing the practice of using AI coding assistants to generate, iterate, and deploy code at dramatically accelerated speeds often with minimal formal review, design documentation, or security scrutiny. The term captures the intuitive, flow-state nature of AI-assisted development. Its security implication, as Koren described, is that inception-to-production cycles that previously took weeks now take days, collapsing the time window available for security review.

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💡Our Insights from this Practitioner 🔍

1. The 25-Minute Window Has Broken the Traditional Security Model

The most operationally significant data point from Elad Koren’s conversation is also the most alarming: Palo Alto’s telemetry shows that once an organization is susceptible to a particular attack pattern, a threat actor can go from initial access to data exfiltration in 25 minutes. This is not a worst-case scenario. It is a measured median.

"It can be seconds. Our latest report shows that within 25 minutes an organization can have data exfiltrated. You cannot wait for the practitioners to fix the gap." Elad Koren

The practical implication for cloud security architecture is that any control requiring human decision-making in the response chain triage ticket, analyst review, change approval cannot be the first line of defense for high-confidence threat signals. The most experienced analyst in your SOC cannot triage, escalate, approve, and contain an incident in 25 minutes when they are also managing a queue of other alerts. The response to known-pattern attacks must be automated.

2. Three Forces Have Changed the Threat Model Permanently

Elad laid out a clear framework for why the security model that worked three years ago is structurally inadequate today, and it is worth internalizing for board-level conversations:

"There are three fundamental things that changed in the model. AI is there for the adversaries they can move much faster. Developers are pushing code much faster with vibe coding. And we are seeing more and more AI applications running in cloud that not many organizations know how to analyze the posture of." Elad Koren

Each of these forces has a distinct security implication that compounds the others. AI-accelerated attacks mean your detection and response must operate at machine speed. Vibe-coded applications mean your code review pipeline will always be behind the deployment pipeline without automation. AI workloads in cloud mean your CSPM and CWPP coverage has gaps in resource types that simply did not exist 18 months ago. Combine all three, and Elad’s conclusion is apt: “combine all three and you have a time bomb basically.”

3. Agentic Cloud Security Is Not a Product Pitch It Is an Architectural Requirement

The framing of “agentic CNAPP” can sound like vendor positioning, but Elad’s description of what it actually means is grounded in operational reality. The core argument is not that AI agents replace your security team. It is that tier-one triage the routine fixes, the known patterns, the high-confidence remediations should be handled autonomously, so that your analysts can focus on the cases that genuinely require human judgment.

"A good solution prioritizes making sure that your tier-one analysts you can take 85, 90% of the things they would do, the regular fixes, automatically. You’ll have AI agents working for you. Because then you’re fighting machines with machines." Elad Koren

Ashish Rajan’s framing of the shift is equally direct: the agentic security era means the CNAPP must have API-level understanding of how AI agents communicate with cloud platforms, not just posture snapshots of static configurations.

"It’s no longer enough that you have a CNAPP. Having an understanding of the pathway, API capabilities, and how you can have AI agents communicate with that as a platform will become the more important thing as we move into 2026 and beyond." Ashish Rajan

4. Visibility and Identity Are the Two Non-Negotiable Foundations

When Elad was asked directly what organizations should focus on for a durable cloud security uplift program, his answer was grounded in a specific real-world example: an experimental AI workload deployed for internal use that was accidentally exposed to the internet because the developer had no security awareness of the infrastructure it was running on.

"That AI workload was open to the world without any need of authentication. The person creating that had little to almost no knowledge or awareness for security. Somebody was able to access it he was able to exfiltrate data. Inception to production in less than three days, including testing, including everything." Elad Koren

His prescription: visibility into where your AI workloads run, identity controls with least-privilege and minimal access for anything that touches those workloads, and securing the infrastructure not just the application. This is not new advice in the abstract, but the AI workload context makes it urgent in a new way. Most organizations’ AI workload inventory is incomplete by definition developers are spinning up new AI-powered services faster than any centralized inventory process can track them.

5. The “Messy Middle” Is Where We Are And That Is a Strategic Opportunity

Elad’s most useful framing for security leaders planning multi-year programs is what he calls the “messy middle”: the transition period between where most organizations are today (siloed, posture-focused, human-speed) and where the industry is going (platformized, agentic, machine-speed). This period is messy because no one knows exactly what the equilibrium looks like. But the direction is clear.

The prescription for this period is not to wait for clarity. It is to build the foundations that will matter regardless of how the technology evolves: unified data platforms that eliminate tool silos, trust in AI-driven automation built through experimentation, and upskilling security practitioners to become orchestrators rather than ticket-processors.

"If organizations continue to build things in silos and look at security as siloed different tasks by different practitioners adversaries will prevail. They’re like water. They’ll just find a path in. You close the door, they look at the window. You close the window, they look at the tunnel. If they don’t have a tunnel, they’ll dig a tunnel." Elad Koren

Palo Alto Networks Cortex Cloud Platform Overview
The platform Elad Koren describes in this episode. Relevant for teams evaluating agentic CNAPP capabilities.

Podcast Episode

Cloud Security Podcast -Full Episode with Elad Koren - Complete transcript and audio for this week's featured conversation

Question for you? (Reply to this email)

🤔 Is your AI workload inventory complete enough that you could answer ‘where does our AI run and who can reach it’ in under an hour?

Next week, we'll explore another critical aspect of cloud security. Stay tuned!

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We would love to hear from you📢 for a feature or topic request or if you would like to sponsor an edition of Cloud Security Newsletter.

Thank you for continuing to subscribe and Welcome to the new members in tis newsletter community💙

Peace!