- Claude Code's exposed source is a call to reassess your AI agent defenses.
Every enterprise running AI coding agents has just lost a layer of defense. On March 31, Anthropic accidentally shipped a 59.8 MB source map file inside version 2.1.88 of its @anthropic-ai/claude-code npm package, exposing 512,000 lines of unobfuscated TypeScript across 1,906 files.
The readable source includes the complete permission model, every bash security validator, 44 unreleased feature flags, and references to upcoming models Anthropic has not announced. Security researcher Chaofan Shou broadcast the discovery on X by approximately 4:23 UTC. Within hours, mirror repositories had spread across GitHub.
Anthropic confirmed the exposure was a packaging error caused by human error. No customer data or model weights were involved. But containment has already failed. The Wall Street Journal reported Wednesday morning that Anthropic had filed copyright takedown requests that briefly resulted in the removal of more than 8,000 copies and adaptations from GitHub.
However, an Anthropic spokesperson told VentureBeat that the takedown was intended to be more limited: "We issued a DMCA takedown against one repository hosting leaked Claude Code source code and its forks. The repo named in the notice was part of a fork network connected to our own public Claude Code repo, so the takedown reached more repositories than intended. We retracted the notice for everything except the one repo we named, and GitHub has restored access to the affected forks."
Programmers have already used other AI tools to rewrite Claude Code's functionality in other programming languages. Those rewrites are themselves going viral. The timing was worse than the leak alone. Hours before the source map shipped, malicious versions of the axios npm package containing a remote access trojan went live on the same registry. Any team that installed or updated Claude Code via npm between 00:21 and 03:29 UTC on March 31 may have pulled both the exposed source and the unrelated axios malware in the same install window.
A same-day Gartner First Take (subscription required) said the gap between Anthropic's product capability and operational discipline should force leaders to rethink how they evaluate AI development tool vendors. Claude Code is the most discussed AI coding agent among Gartner's software engineering clients. This was the second leak in five days. A separate CMS misconfiguration had already exposed nearly 3,000 unpublished internal assets, including draft announcements for an unreleased model called Claude Mythos. Gartner called the cluster of March incidents a systemic signal.
What 512,000 lines reveal about production AI agent architecture
The leaked codebase is not a chat wrapper. It is the agentic harness that wraps Claude's language model and gives it the ability to use tools, manage files, execute bash commands, and orchestrate multi-agent workflows. The WSJ described the harness as what allows users to control and direct AI models, much like a harness allows a rider to guide a horse. Fortune reported that competitors and legions of startups now have a detailed road map to clone Claude Code's features without reverse engineering them.
The components break down fast. A 46,000-line query engine handles context management through three-layer compression and orchestrates 40-plus tools, each with self-contained schemas and per-tool granular permission checks. And 2,500 lines of bash security validation run 23 sequential checks on every shell command, covering blocked Zsh builtins, Unicode zero-width space injection, IFS null-byte injection, and a malformed token bypass discovered during a HackerOne review.
Gartner caught a detail most coverage missed. Claude Code is 90% AI-generated, per Anthropic's own public disclosures. Under the current U.S. copyright law requiring human authorship, the leaked code carries diminished intellectual property protection. The Supreme Court declined to revisit the human authorship standard in March 2026. Every organization shipping AI-generated production code faces this same unresolved IP exposure.
Three attack paths, the readable source makes it cheaper to exploit
The minified bundle already shipped with every string literal extractable. What the readable source eliminates is the research cost. A technical analysis from Straiker's Jun Zhou, an agentic AI security company, mapped three compositions that are now practical, not theoretical, because the implementation is legible.
Context poisoning via the compaction pipeline. Claude Code manages context pressure through a four-stage cascade. MCP tool results are never microcompacted. Read tool results skip budgeting entirely. The autocompact prompt instructs the model to preserve all user messages that are not tool results. A poisoned instruction in a cloned repository's CLAUDE.md file can survive compaction, get laundered through summarization, and emerge as what the model treats as a genuine user directive. The model is not jailbroken. It is cooperative and follows what it believes are legitimate instructions.
Sandbox bypass through shell parsing differentials. Three separate parsers handle bash commands, each with different edge-case behavior. The source documents a known gap where one parser treats carriage returns as word separators, while bash does not. Alex Kim's review found that certain validators return early-allow decisions that short-circuit all subsequent checks. The source contains explicit warnings about the past exploitability of this pattern.
The composition. Context poisoning instructs a cooperative model to construct bash commands sitting in the gaps of the security validators. The defender's mental model assumes an adversarial model and a cooperative user. This attack inverts both. The model is cooperative. The context is weaponized. The outputs look like commands a reasonable developer would approve.
Elia Zaitsev, CrowdStrike's CTO, told VentureBeat in an exclusive interview at RSAC 2026 that the permission problem exposed in the leak reflects a pattern he sees across every enterprise deploying agents. "Don't give an agent access to everything just because you're lazy," Zaitsev said. "Give it access to only what it needs to get the job done." He warned that open-ended coding agents are particularly dangerous because their power comes from broad access. "People want to give them access to everything. If you're building an agentic application in an enterprise, you don't want to do that. You want a very narrow scope."
Zaitsev framed the core risk in terms that the leaked source validates. "You may trick an agent into doing something bad, but nothing bad has happened until the agent acts on that," he said. That is precisely what the Straiker analysis describes: context poisoning turns the agent cooperative, and the damage happens when it executes bash commands through the gaps in the validator chain.
What the leak exposed and what to audit
The table below maps each exposed layer to the attack path it enables and the audit action it requires. Print it. Take it to Monday's meeting.
Exposed Layer
What the Leak Revealed
Attack Path Enabled
Defender Audit Action
4-stage compaction pipeline
Exact criteria for what survives each stage. MCP tool results are never microcompacted. Read results, skip budgeting.
Context poisoning: malicious instructions in CLAUDE.md survive compaction and get laundered into 'user directives'.
Audit every CLAUDE.md and .claude/config.json in cloned repos. Treat as executable, not metadata.
Bash security validators (2,500 lines, 23 checks)
Full validator chain, early-allow short circuits, three-parser differentials, blocked pattern lists
Sandbox bypass: CR-as-separator gap between parsers. Early-allow in git validators bypasses all downstream checks.
Restrict broad permission rules (Bash(git:*), Bash(echo:*)). Redirect operators chain with allowed commands to overwrite files.
MCP server interface contract
Exact tool schemas, permission checks, and integration patterns for all 40+ built-in tools
Malicious MCP servers that match the exact interface. Supply chain attacks are indistinguishable from legitimate servers.
Treat MCP servers as untrusted dependencies. Pin versions. Monitor for changes. Vet before enabling.
44 feature flags (KAIROS, ULTRAPLAN, coordinator mode)
Unreleased autonomous agent mode, 30-min remote planning, multi-agent orchestration, background memory consolidation
Competitors accelerate the development of comparable features. Future attack surface previewed before defenses ship.
Monitor for feature flag activation in production. Inventory where agent permissions expand with each release.
Anti-distillation and client attestation
Fake tool injection logic, Zig-level hash attestation (cch=00000), GrowthBook feature flag gating
Workarounds documented. MITM proxy strips anti-distillation fields. Env var disables experimental betas.
Do not rely on vendor DRM for API security. Implement your own API key rotation and usage monitoring.
Undercover mode (undercover.ts)
90-line module strips AI attribution from commits. Force ON possible, force OFF impossible. Dead-code-eliminated in external builds.
AI-authored code enters repos with no attribution. Provenance and audit trail gaps for regulated industries.
Implement commit provenance verification. Require AI disclosure policies for development teams using any coding agent.
AI-assisted code is already leaking secrets at double the rate
GitGuardian's State of Secrets Sprawl 2026 report, published March 17, found that Claude Code-assisted commits leaked secrets at a 3.2% rate versus the 1.5% baseline across all public GitHub commits. AI service credential leaks surged 81% year-over-year to 1,275,105 detected exposures. And 24,008 unique secrets were found in MCP configuration files on public GitHub, with 2,117 confirmed as live, valid credentials. GitGuardian noted the elevated rate reflects human workflow failures amplified by AI speed, not a simple tool defect.
The operational pattern Gartner is tracking
Feature velocity compounded the exposure. Anthropic shipped over a dozen Claude Code releases in March, introducing autonomous permission delegation, remote code execution from mobile devices, and AI-scheduled background tasks. Each capability widened the operational surface. The same month that introduced them produced the leak that exposed their implementation.
Gartner's recommendation was specific. Require AI coding agent vendors to demonstrate the same operational maturity expected of other critical development infrastructure: published SLAs, public uptime history, and documented incident response policies. Architect provider-independent integration boundaries that would let you change vendors within 30 days. Anthropic has published one postmortem across more than a dozen March incidents. Third-party monitors detected outages 15 to 30 minutes before Anthropic's own status page acknowledged them.
The company riding this product to a $380 billion valuation and a possible public offering this year, as the WSJ reported, now faces a containment battle that 8,000 DMCA takedowns have not won.
Merritt Baer, Chief Security Officer at Enkrypt AI, an enterprise AI guardrails company, and a former AWS security leader, told VentureBeat that the IP exposure Gartner flagged extends into territory most teams have not mapped. "The questions many teams aren't asking yet are about derived IP," Baer said. "Can model providers retain embeddings or reasoning traces, and are those artifacts considered your intellectual property?" With 90% of Claude Code's source AI-generated and now public, that question is no longer theoretical for any enterprise shipping AI-written production code.
Zaitsev argued that the identity model itself needs rethinking. "It doesn't make sense that an agent acting on your behalf would have more privileges than you do," he told VentureBeat. "You may have 20 agents working on your behalf, but they're all tied to your privileges and capabilities. We're not creating 20 new accounts and 20 new services that we need to keep track of." The leaked source shows Claude Code's permission system is per-tool and granular. The question is whether enterprises are enforcing the same discipline on their side.
Five actions for security leaders this week
1. Audit CLAUDE.md and .claude/config.json in every cloned repository. Context poisoning through these files is a documented attack path with a readable implementation guide. Check Point Research found that developers inherently trust project configuration files and rarely apply the same scrutiny as application code during reviews.
2. Treat MCP servers as untrusted dependencies. Pin versions, vet before enabling, monitor for changes. The leaked source reveals the exact interface contract.
3. Restrict broad bash permission rules and deploy pre-commit secret scanning. A team generating 100 commits per week at the 3.2% leak rate is statistically exposing three credentials. MCP configuration files are the newest surface that most teams are not scanning.
4. Require SLAs, uptime history, and incident response documentation from your AI coding agent vendor. Architect provider-independent integration boundaries. Gartner's guidance: 30-day vendor switch capability.
5. Implement commit provenance verification for AI-assisted code. The leaked Undercover Mode module strips AI attribution from commits with no force-off option. Regulated industries need disclosure policies that account for this.
Source map exposure is a well-documented failure class caught by standard commercial security tooling, Gartner noted. Apple and identity verification provider Persona suffered the same failure in the past year. The mechanism was not novel. The target was. Claude Code alone generates an estimated $2.5 billion in annualized revenue for a company now valued at $380 billion. Its full architectural blueprint is circulating on mirrors that have promised never to come down.
- Anthropic enlists a dozen partners to secure critical infrastructure with an unreleased AI model.
Anthropic on Tuesday announced Project Glasswing, a sweeping cybersecurity initiative that pairs an unreleased frontier AI model — Claude Mythos Preview — with a coalition of twelve major technology and finance companies in an effort to find and patch software vulnerabilities across the world's most critical infrastructure before adversaries can exploit them.
The launch partners include Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, Nvidia, and Palo Alto Networks. Anthropic says it has also extended access to more than 40 additional organizations that build or maintain critical software, and is committing up to $100 million in usage credits for Claude Mythos Preview across the effort, along with $4 million in direct donations to open-source security organizations.
The announcement arrives at a moment of extraordinary momentum — and extraordinary scrutiny — for the San Francisco-based AI startup. Anthropic disclosed on Sunday that its annualized revenue run rate has surpassed $30 billion, up from approximately $9 billion at the end of 2025, and the number of business customers each spending over $1 million annually now exceeds 1,000, doubling in less than two months. The company simultaneously announced a multi-gigawatt compute deal with Google and Broadcom. On the same day, Bloomberg reported that Anthropic had poached a senior Microsoft executive, Eric Boyd, to lead its infrastructure expansion.
But Glasswing is something categorically different from a revenue milestone or a compute deal. It’s Anthropic's most ambitious attempt to translate frontier AI capabilities — capabilities the company itself describes as dangerous — into a defensive advantage before those same capabilities proliferate to hostile actors.
Why Anthropic built a model it considers too dangerous to release publicly
At the center of Project Glasswing sits Claude Mythos Preview, a general-purpose frontier model that Anthropic says has already identified thousands of high-severity zero-day vulnerabilities — meaning flaws previously unknown to software developers — in every major operating system and every major web browser, along with a range of other critical software.
The company is not making the model generally available.
"We do not plan to make Claude Mythos Preview generally available due to its cybersecurity capabilities," Newton Cheng, Frontier Red Team Cyber Lead at Anthropic, told VentureBeat in an exclusive interview. "However, given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout — for economies, public safety, and national security — could be severe."
That language — "the fallout could be severe" — is striking coming from the company that built the model. Anthropic is effectively arguing that the tool it created is powerful enough to reshape the cybersecurity landscape, and that the only responsible thing to do is to keep it restricted while giving defenders a head start.
The technical results reinforce that claim. According to Anthropic's press release, Mythos Preview was able to find nearly all of the vulnerabilities it surfaced, and develop many related exploits, entirely autonomously, without any human steering. Three examples stand out: The model found a 27-year-old vulnerability in OpenBSD — widely regarded as one of the most security-hardened operating systems in the world and commonly used to run firewalls and critical infrastructure. The flaw allowed an attacker to remotely crash any machine running the OS simply by connecting to it. It also discovered a 16-year-old vulnerability in FFmpeg — the near-ubiquitous video encoding and decoding library — in a line of code that automated testing tools had exercised five million times without ever catching the problem. And perhaps most alarmingly, Mythos Preview autonomously found and chained together several vulnerabilities in the Linux kernel to escalate from ordinary user access to complete control of the machine.
All three vulnerabilities have been reported to the relevant maintainers and have since been patched. For many other vulnerabilities still in the remediation pipeline, Anthropic says it is publishing cryptographic hashes of the details today, with plans to reveal specifics after fixes are in place.
On the CyberGym evaluation benchmark, Mythos Preview scored 83.1%, compared to 66.6% for Claude Opus 4.6, Anthropic's next-best model. The gap is even wider on coding benchmarks: Mythos Preview achieves 93.9% on SWE-bench Verified versus 80.8% for Opus 4.6, and 77.8% on SWE-bench Pro versus 53.4%.
How Anthropic plans to disclose thousands of zero-days without overwhelming open-source maintainers
Finding thousands of zero-days at once sounds impressive. Actually handling the output responsibly is a logistical nightmare — and one of the sharpest criticisms that security researchers have raised about AI-driven vulnerability discovery. Flooding open-source maintainers, many of whom are unpaid volunteers, with an avalanche of critical bug reports could easily do more harm than good.
Cheng told VentureBeat that Anthropic has built a triage pipeline specifically to manage this problem. "We triage every bug that we find and then send the highest severity bugs to professional human triagers we have contracted to assist in our disclosure process by manually validating every bug report before we send it out to ensure that we send only high-quality reports to maintainers," he said.
That pipeline is designed to prevent exactly the scenario that maintainers fear most: an automated firehose of unverified reports. "We do not submit large volumes of findings to a single project without first reaching out in an effort to agree on a pace the maintainer can sustain," Cheng added.
When Anthropic has access to the source code, the company aims to include a candidate patch with every report, labeled by provenance — meaning the maintainer knows the patch was written or reviewed by a model — and offers to collaborate on a production-quality fix. "Models can write patches," Cheng noted, "but there are many factors that impact patch quality, and we strongly recommend that autonomously-written patches are put under the same scrutiny and testing that human-written patches are."
On disclosure timelines, Anthropic says it follows a coordinated vulnerability disclosure framework. Once a patch is available, the company will generally wait 45 days before publishing full technical details, giving downstream users time to deploy the fix before exploitation information becomes public. Cheng said the company may shorten that buffer "if the details are already publicly known through other channels, or if earlier publication would materially help defenders identify and mitigate ongoing attacks," or extend it "when patch deployment is unusually complex or the affected footprint is unusually broad."
Those are reasonable principles, but they will be tested at a scale that no vulnerability disclosure program has ever attempted. The sheer volume of findings — thousands of zero-days across every major platform — means that even a well-designed triage process will face bottlenecks. And the 45-day disclosure window assumes that maintainers can actually produce, test, and ship a patch in that time, which is far from guaranteed for complex kernel-level bugs or deeply embedded cryptographic flaws.
The source code leak, the CMS blunder, and why trust is Anthropic's biggest vulnerability
The irony of a company claiming to build the most capable cyber model ever constructed while simultaneously suffering a string of embarrassing security lapses has not been lost on observers.
In late March, a draft blog post about Mythos was left in an unsecured and publicly searchable data store — a CMS misconfiguration that exposed roughly 3,000 internal assets, including what appeared to be strategic plans for the model's rollout. Days later, on March 31, anyone who ran npm install on Claude Code pulled down Anthropic's complete original source code — 512,000 lines — for approximately three hours due to a packaging error, an incident that drew widespread attention in the developer community and was first reported by VentureBeat.
When asked why partners and governments should trust Anthropic as the custodian of a model it describes as having unprecedented cyber capabilities, Cheng was direct. "Security is central to how we build and ship," he told VentureBeat. "These two incidents, a blog CMS misconfiguration and an npm packaging error, were human errors in publishing tooling, not breaches of our security architecture. We've made changes to prevent these from happening again, and we'll continue to improve our processes."
It is a technically accurate distinction — neither incident involved a breach of Anthropic's core model weights, training infrastructure, or API systems — but it is also a distinction that may prove difficult to sustain as a public argument. For an organization asking governments and Fortune 500 companies to trust it with a tool that can autonomously find and exploit vulnerabilities in the Linux kernel, even minor operational lapses carry outsized reputational risk. The fact that the Mythos leak itself was what first alerted the security community to the model's existence, weeks before the planned announcement, underscores the point.
What Microsoft, CrowdStrike, and the Linux Foundation found when they tested the model
The coalition's breadth is notable. It includes direct competitors — Google and Microsoft — alongside cybersecurity incumbents, financial institutions, and the steward of the world's largest open-source ecosystem. And several partners have already been running Mythos Preview against their own infrastructure for weeks.
CrowdStrike's CTO Elia Zaitsev framed the initiative in terms of collapsing timelines: "The window between a vulnerability being discovered and being exploited by an adversary has collapsed — what once took months now happens in minutes with AI." AWS Vice President and CISO Amy Herzog said her teams have already been testing Mythos Preview against critical codebases, where the model is "already helping us strengthen our code." And Microsoft's Global CISO Igor Tsyganskiy noted that when tested against CTI-REALM, Microsoft's open-source security benchmark, "Claude Mythos Preview showed substantial improvements compared to previous models."
Perhaps the most revealing comment came from Jim Zemlin, CEO of the Linux Foundation, who pointed to the fundamental asymmetry that has plagued open-source security for decades: "In the past, security expertise has been a luxury reserved for organizations with large security teams. Open-source maintainers — whose software underpins much of the world's critical infrastructure — have historically been left to figure out security on their own." Project Glasswing, he said, "offers a credible path to changing that equation."
To back that claim with dollars, Anthropic says it has donated $2.5 million to Alpha-Omega and OpenSSF through the Linux Foundation, and $1.5 million to the Apache Software Foundation. Maintainers interested in access can apply through Anthropic's Claude for Open Source program.
Inside the pricing, the compute deal, and Anthropic's path toward a potential IPO
After the research preview period — during which Anthropic's $100 million credit commitment will cover most usage — Claude Mythos Preview will be available to participants at $25 per million input tokens and $125 per million output tokens. Participants can access the model through the Claude API, Amazon Bedrock, Google Cloud's Vertex AI, and Microsoft Foundry.
Those prices reflect the model's computational intensity. The draft blog post that leaked in March described Mythos as a large, compute-intensive model that would be expensive for both Anthropic and its customers to serve. Anthropic's solution is to develop and launch new safeguards with an upcoming Claude Opus model, allowing the company to "improve and refine them with a model that does not pose the same level of risk as Mythos Preview," as Cheng told VentureBeat. Security professionals whose legitimate work is affected by those safeguards will be able to apply to an upcoming Cyber Verification Program.
The financial context matters. The same day Project Glasswing launched, Anthropic disclosed its revenue milestone and the Google-Broadcom compute deal. Broadcom signed an expanded deal with Anthropic that will give the AI startup access to about 3.5 gigawatts worth of computing capacity drawing on Google's AI processors, according to CNBC. The scale of compute being marshaled is staggering — and it helps explain why Anthropic needs both the revenue from enterprise cybersecurity partnerships and the infrastructure to serve a model of Mythos Preview's size.
The timing also intersects with growing speculation about Anthropic's path to a public offering. The company is reportedly evaluating an IPO as early as October 2026. A high-profile, government-adjacent cybersecurity initiative with blue-chip partners is exactly the kind of program that burnishes an IPO narrative — particularly when the company can simultaneously point to $30 billion in annualized revenue and a compute footprint measured in gigawatts.
Anthropic says defenders have months, not years, before adversaries catch up
The most consequential question raised by Project Glasswing is not whether Mythos Preview's capabilities are real — the partner endorsements and patched vulnerabilities suggest they are — but how much time defenders actually have before similar capabilities are available to adversaries.
Cheng was candid about the timeline. "Frontier AI capabilities are likely to advance substantially over just the next few months," he told VentureBeat. "Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely." He described Project Glasswing as "an important step toward giving defenders a durable advantage in the coming AI-driven era of cybersecurity" but added a crucial caveat: "It's important to note, this is a starting point. No one organization can solve these cybersecurity problems alone."
That framing — months, not years — is worth taking seriously. DARPA launched its original Cyber Grand Challenge in 2016, a competition to create automatic defensive systems capable of reasoning about flaws, formulating patches, and deploying them on a network in real time. At the time, the winning AI-powered bot, Mayhem, finished last when placed against human teams at DEF CON. A decade later, Anthropic is claiming that a frontier AI model can find vulnerabilities that survived 27 years of expert human review and millions of automated security tests — and can chain exploits together autonomously to achieve full system compromise.
The delta between those two data points illustrates why the industry is treating this as a genuine inflection point, not a marketing exercise. Anthropic itself has firsthand experience with the offensive side of this equation: the company disclosed in November 2025 that a Chinese state-sponsored group achieved 80 to 90 percent autonomous tactical execution using Claude across approximately 30 targets, according to Anthropic's misuse report.
Project Glasswing arrives during one of the most turbulent weeks in Anthropic's history. In the span of days, the company has announced a model it considers too dangerous for public release, disclosed that its revenue has tripled, sealed a multi-gigawatt compute deal, hired a senior Microsoft executive, made it more expensive for Claude Code subscribers to use third-party tools like OpenClaw, and weathered a major outage of its Claude chatbot on Tuesday morning. Anthropic says it will report publicly on what it has learned within 90 days. In the medium term, the company has proposed that an independent, third-party body might be the ideal home for continued work on large-scale cybersecurity projects.
Whether any of that is fast enough depends on a race that is already underway. Anthropic built a model that can autonomously crack open the most hardened operating systems on the planet — and is now betting that sharing it with defenders, under careful restrictions, will do more good than the inevitable moment when similar capabilities land in less careful hands. It is, in essence, a wager that transparency can outrun proliferation. The next few months will determine whether that bet pays off, or whether the glasswing's wings were never quite opaque enough to hide what was coming.
- Three security blind spots in Claude expose what your data stack misses
Between May 6 and 7, four security research teams published findings about Anthropic’s Claude that most outlets covered as three separate stories. One involved a water utility in Mexico, another targeted a Chrome extension, and a third hijacked OAuth tokens through Claude Code. In one case, Claude identified a water utility’s SCADA gateway without being told to look for one.
These are not three bugs. They are one architectural question playing out on three surfaces. No single patch released so far addresses all of them.
The common thread is the confused deputy, a trust-boundary failure where a program with legitimate authority executes actions on behalf of the wrong principal. In each case, Claude held real capabilities on every surface and handed them to whoever showed up. An attacker probing a water utility's network. A Chrome extension with zero permissions. A malicious npm package rewriting a config file.
Carter Rees, VP of Artificial Intelligence at Reputation, identified the structural reason this class of failure is so dangerous. The flat authorization plane of an LLM fails to respect user permissions, Rees told VentureBeat in an exclusive interview. An agent operating on that flat plane does not need to escalate privileges, it already has them.
Kayne McGladrey, an IEEE senior member who advises enterprises on identity risk, described the same dynamic independently in an interview with VentureBeat. Enterprises are cloning human permission sets onto agentic systems, McGladrey said. The agent does whatever it needs to do to get its job done, and sometimes that means using far more permissions than a human would.
Dragos found Claude targeting a water utility’s SCADA gateway without being told to look for one
Dragos published its analysis on May 6. Between December 2025 and February 2026, an unidentified adversary compromised multiple Mexican government organizations. In January 2026, the campaign reached Servicios de Agua y Drenaje de Monterrey, the municipal water and drainage utility serving the Monterrey metropolitan area.
Dragos analyzed more than 350 artifacts. The adversary used Claude as the primary technical executor and OpenAI’s GPT models for data processing. Claude wrote a 17,000-line Python framework containing 49 modules for network discovery, credential harvesting, privilege escalation, and lateral movement. Claude compressed what would traditionally take days or weeks of tooling development into hours, according to the Dragos analysis.
Without any prior ICS/OT context, Claude identified a server running a vNode SCADA/IIoT management interface, classified the platform as high-value, generated credential lists, and launched an automated password spray. The attack failed, and no OT breach occurred, but Claude did the targeting. Dragos noted that this was not a product vulnerability in the traditional sense because Claude performed exactly as designed. The architectural gap, as the firm described it, is that the model cannot distinguish an authorized developer from an adversary using the same interface.
Jay Deen, associate principal adversary hunter at Dragos, wrote that the investigation showed how commercial AI tools have made OT more visible to adversaries already operating within IT.
CrowdStrike CTO Elia Zaitsev told VentureBeat why this class of incident evades detection. Nothing bad has happened until the agent acts, Zaitsev said. It is almost always at the action layer. The Monterrey reconnaissance looked like a developer querying internal systems. The developer tool just had an adversary at the keyboard.
Stack blind spot: OT monitoring does not flag AI-generated recon from IT-side developer tools. EDR sees the process but has no visibility into intent.
LayerX proved any Chrome extension can hijack Claude through a trust boundary Anthropic partially patched
On May 7, LayerX researcher Aviad Gispan disclosed ClaudeBleed. Claude in Chrome uses Chrome’s externally connectable feature to allow communication with scripts on the claude.ai origin, but does not verify whether those scripts came from Anthropic or were injected by another extension. Any Chrome extension can inject commands into Claude’s messaging interface. Zero permissions required.
LayerX reported the flaw on April 27. Anthropic shipped version 1.0.70 on May 6. LayerX found that the patch did not remove the vulnerable handler. LayerX bypassed the new protections through the side-panel initialization flow and by switching Claude into "Act without asking" mode, which required no user notification. Anthropic's patch survived less than a day.
Mike Riemer, SVP of Network Security Group and Field CISO at Ivanti, told VentureBeat that threat actors are now reverse engineering patches within 72 hours using AI assistance. If a vendor releases a patch and the customer has not applied it within that window, the vulnerability is already being exploited, Riemer said. Anthropic's ClaudeBleed patch did not survive even a third of that window.
Stack blind spot: EDR watches files and processes but does not monitor extension-to-extension messaging within the browser. ClaudeBleed produces no file writes, no network anomalies, and no process spawns.
Mitiga showed a config file rewrite steals OAuth tokens and survives rotation
Also on May 7, Mitiga Labs researcher Idan Cohen published a man-in-the-middle attack chain targeting Claude Code. Claude Code stores MCP configuration and OAuth tokens in ~/.claude.json, a single user-writable file. A malicious npm postinstall hook can rewrite the MCP server URL to route traffic through an attacker's proxy, capturing OAuth tokens for Jira, Confluence, and GitHub. Because the postinstall hook fires on every Claude Code load, it reasserts the malicious endpoint even after token rotation — meaning the standard incident response step of rotating credentials does not break the attack chain unless the hook itself is removed first.
Mitiga reported the finding on April 10. On April 12, Anthropic classified it as out of scope, according to Mitiga’s published disclosure.
Riemer described the principle this chain violates. I do not know you until I validate you, Riemer told VentureBeat. Until I know what it is and I know who is on the other side of the keyboard, I am not going to communicate with it. The ~/.claude.json rewrite substitutes the attacker’s endpoint for the legitimate one. Claude Code never re-validates.
Riemer has spent 21 years architecting the product he now leads and holds five patents on its security infrastructure. He applies the same defensive logic he built into his own platform. If a threat actor gets in, drop all connections. That is a fail-safe design. Anthropic's architecture does the opposite. It fails open.
Stack blind spot: Web application firewalls never see local config rewrites. EDR treats JSON file writes as normal developer behavior. Rotating tokens does not break the chain unless responders also confirm the hook is removed.
Anthropic’s response pattern treats the user’s trust decision as the security boundary
Anthropic classified Mitiga's MCP token theft as out of scope on April 12. The company called OX Security's STDIO vulnerability affecting an estimated 200,000 MCP servers "expected" and by design. Anthropic declined Adversa AI's TrustFall as outside its threat model, according to Adversa's published disclosure. ClaudeBleed was partially patched. Across all four disclosures, the researchers say the underlying trust model remains exploitable.
Alex Polyakov, co-founder of Adversa AI, told The Register that each vulnerability gets patched in isolation, but the underlying class has not been fixed.
Zaitsev offered a frame for why consent alone cannot serve as the trust boundary. If you think you can always understand intent, Zaitsev told VentureBeat, then you would also think it is possible to write a program that reads a text transcript and figures out if someone is lying. That is intuitively an impossible problem to solve.
Adversa AI showed that a cloned repo can auto-execute arbitrary code the moment a developer clicks trust
Adversa AI researcher Alex Polyakov published TrustFall, demonstrating that project-scoped Claude configuration files in a cloned repository can silently authorize MCP servers to run as native OS processes with full user privileges. The moment a developer clicks the generic “Yes, I trust this folder” dialog, any MCP server defined in the project config launches. The dialog does not show what it authorizes.
In automated build pipelines where Claude Code runs without a screen, the trust dialog never appears. The attack executes with zero human interaction. Adversa confirmed the pattern is not unique to Claude Code. All four major coding agents (Claude Code, Cursor, Gemini CLI, and GitHub Copilot) can auto-execute project-defined MCP servers the moment a developer accepts that dialog.
Stack blind spot: No current security tooling can tell the difference between a legitimate project config and a malicious one. The trust dialog is the only thing standing between the developer and arbitrary code execution, and it does not show what it is about to authorize.
The matrix below maps each surface that Claude wrongly trusted, the stack blind spot, the detection signal, and the recommended action.
Claude Confused Deputy Audit Matrix
Surface
Who Claude Trusted
Why Your Stack Misses It
Detection Signal
Recommended Action
claude.ai / API
Dragos, May 6
350+ artifacts analyzed
Attacker posing as an authorized user via Claude’s prompt interface.
Claude cannot distinguish a developer mapping internal systems from an adversary doing the same thing through the same interface.
OT monitoring watches ICS protocols and anomalous traffic patterns.
AI-generated recon originates from an IT-side developer tool, not from the OT network. The queries look identical to legitimate developer activity because they ARE legitimate developer activity with an adversary at the keyboard.
Query:
Claude API logs for requests referencing internal hostnames, IP ranges, or SCADA/ICS keywords.
Alert trigger:
>5 credential generation requests against internal services in 60 minutes.
Escalation:
OT team notified on any AI-originated query touching vNode, SCADA, HMI, or PLC keywords.
Segment AI-assisted sessions from OT-adjacent network segments.
Log all Claude API calls referencing internal hostnames or IP ranges.
Alert on automated credential generation targeting internal authentication interfaces.
Require explicit OT authorization for any AI tool with internal network access.
Claude in Chrome
LayerX, May 7
v1.0.70 patch bypassed <24hrs
Any script running in the claude.ai browser context, including scripts injected by zero-permission extensions.
The externally connectable manifest trusts the origin (claude.ai), not the execution context. Any extension can inject into that origin.
EDR monitors file system activity, process execution, and network connections.
Extension-to-extension messaging happens entirely within the browser runtime. No file writes. No network anomalies. No process spawns. EDR has zero visibility into Chrome’s internal messaging API.
Query:
Chrome extension inventory for any extension with content scripts targeting claude.ai in the manifest.
Alert trigger:
New extension installed with claude.ai in permissions or content script targets.
Escalation:
Browser security team reviews any extension communicating with Claude’s messaging interface.
Audit Chrome extensions across the fleet for claude.ai content script access.
Disable “Act without asking” mode in Claude in Chrome enterprise-wide.
Deploy browser security tooling that inspects extension messaging channels.
Monitor for extensions injecting content scripts into claude.ai domain.
Claude Code MCP
Mitiga, May 7
Anthropic: “out of scope” April 12
Rewritten ~/.claude.json routing MCP traffic through attacker-controlled proxy.
Claude Code reads the MCP server URL from the config file on every load. It never re-validates that the URL matches the endpoint the user originally authorized.
WAF inspects HTTP traffic between clients and servers. It never sees a local config file rewrite.
EDR treats JSON file writes in the user’s home directory as normal developer behavior. Token rotation feeds the chain because the npm postinstall hook reasserts the malicious URL on every Claude Code load.
Query:
File integrity monitor on ~/.claude.json for MCP server URL changes.
Alert trigger:
MCP server URL changed to endpoint not on approved allowlist.
Escalation:
IR team confirms postinstall hook removal before closing ticket. Token rotation alone is insufficient.
Monitor ~/.claude.json for unexpected MCP endpoint changes against an allowlist.
Block or alert on npm postinstall hooks that modify files outside the package directory.
Maintain a centralized MCP server URL allowlist.
Do NOT assume token rotation breaks the chain without confirming the malicious hook is removed first.
Claude Code project settings
Adversa AI, May 7
Affects Claude, Cursor, Gemini CLI, Copilot
Project-scoped .claude configuration file in a cloned repository.
Clicking the generic “Yes, I trust this folder” dialog silently authorizes any MCP server defined in the project config. The dialog does not show what it authorizes.
No current security tooling can tell the difference between a legitimate project config and a malicious one.
In automated build pipelines, Claude Code runs without a screen. The attack executes with zero human interaction against pull-request branches.
Query:
Pre-clone scan for .claude, .claude.json, .mcp.json, CLAUDE.md files in repository root.
Alert trigger:
Repo contains MCP server definition not on approved organizational list.
Escalation:
DevSecOps reviews before any developer opens the repo in Claude Code or any coding agent.
Scan cloned repositories for .claude configuration files before opening in any AI coding agent.
Require explicit per-server MCP approval rather than blanket folder trust.
Flag repos that define custom MCP servers in project configuration.
Audit CI/CD pipelines running Claude Code headless where trust dialogs are skipped entirely.
The deputy changed
Norm Hardy described the confused deputy in 1988. The deputy he had in mind was a compiler. This one writes 17,000-line exploitation frameworks, identifies SCADA gateways on its own, and holds OAuth tokens to Jira, Confluence, and GitHub. Four research teams found the same failure class on four surfaces in the same week. Anthropic's response to each one was some version of "the user consented." The matrix above is the audit Anthropic has not built. If your team runs Claude Code or Claude in Chrome, start there.
