At Black Hat USA 2026, Novee Security researcher Elad Meged presented what may be the most uncomfortable finding in the agentic AI security space: you can give an AI coding agent a perfect security posture, and it will still leak your secrets if your CI/CD harness has any of these vulnerability classes.

The research is called Cordyceps — named for the fungus that takes over its host. The name is apt. These vulnerabilities don’t compromise the AI model. They compromise the harness around it, the infrastructure that runs the model and connects it to your codebase, your secrets, and your supply chain.

Here’s what Novee found, what it means for your pipelines, and the questions you should be asking before deploying any AI coding agent in your automation.

The Core Finding

Novee scanned approximately 654 high-impact public repositories and found that 300+ were fully exploitable by unauthenticated attackers via malicious pull requests or comments alone. No special privileges required.

Affected repositories included:

  • Microsoft Azure Sentinel
  • Google ADK samples
  • Apache Doris
  • Cloudflare Workers SDK
  • Python Software Foundation repositories

The attack surface exists wherever an AI coding agent reads untrusted content — an issue description, a PR comment, a commit message — and that content can reach a workflow that executes with elevated permissions.

The Vulnerability Classes

Novee identified three broad classes of CI/CD workflow vulnerabilities that Cordyceps exploits:

1. Command Injection Through Agent Input

AI coding agents accept natural language input and translate it into shell commands. When those commands are executed with prefix-match approval logic rather than exact command allowlisting, injected content inside what the agent considers “safe” string boundaries can execute arbitrary code.

Novee demonstrated this against Anthropic’s own Claude Code Action on the anthropics/claude-code repository. The finding, CVE-2026-54316, resulted in three rounds of patch-and-bypass — each Anthropic fix exposed the next hidden assumption. The researchers achieved:

  • Remote code execution on Anthropic’s own CI runner
  • Arbitrary file reads
  • Theft of ANTHROPIC_API_KEY and GITHUB_TOKEN

The key technical insight: Claude Code’s security validator runs 23 checks, but applies them only to unquoted content. Content inside single quotes is stripped before validation on the assumption that single-quoted strings are inert to the shell. That assumption breaks when content is passed to programs that interpret their arguments differently than bash does.

2. Broken Authorization at Workflow Handoffs

The most dangerous pattern Novee found isn’t misconfiguration — it’s correct security decisions that stop being correct at handoffs.

The Google Gemini CLI finding is the clearest example. Google rated it CVSS 10.0 in their own advisory (GHSA-wpqr-6v78-jr5g). The vulnerability allowed supply-chain compromise of google-gemini/gemini-cli, which has approximately 2 million monthly downstream installs. Google’s response was a breaking change to the trust model for headless execution — not a workflow patch — because the flaw lived in the product itself, not in any individual configuration choice.

The pattern: a trust decision that’s valid in one execution context gets inherited by a downstream workflow step that operates under different assumptions. No one misconfigured anything. The handoff just didn’t carry the right constraints forward.

3. Cross-Workflow Privilege Escalation via Writable Instruction Files

The OpenAI Codex finding demonstrates the most persistent pattern. When an agent’s instruction file is writable and lives on a shared filesystem, one poisoned execution stage can inject standing instructions that control every subsequent agent run.

Per Novee’s research: “A writable instruction file turns one poisoned stage into standing control of the next agent’s instructions, and every action it takes.” The sandbox behaved as documented throughout — which is exactly the problem. The documented default behavior enables the attack.

What This Means for Your Pipelines

The AI Agent Amplification Effect

The reason Cordyceps matters beyond standard CI/CD security is the AI amplification dimension. An AI coding agent that reproduces code patterns at scale will also reproduce insecure workflow patterns at scale. If the agent has read access to your workflow files, it may suggest changes that preserve existing vulnerabilities — or introduce new ones modeled on the insecure patterns it learned from.

This is the core Cordyceps concern: that AI agents operating in the context of CI/CD automation inherit, spread, and regenerate the vulnerability classes Novee documented.

Questions to Ask Before Deploying AI Coding Agents

Before enabling any AI coding agent in your GitHub Actions or CI/CD automation, Novee’s research suggests asking:

On trust boundaries:

  • What untrusted content can reach the agent? (issue titles, PR bodies, commit messages, review comments)
  • Does the workflow fire on external contributor events?
  • Can a pull_request event from a fork trigger the agent?

On command execution:

  • Is the agent permitted to execute shell commands?
  • Are those commands approved by prefix match or by exact allowlist?
  • Does the agent shell out to tools that interpret their arguments differently from bash?

On secrets and tokens:

  • What secrets are in scope for the workflow the agent runs in?
  • Does the agent have write-back access to the repository?
  • Can an agent action trigger a workflow with higher permissions than the triggering event warrants?

On instruction persistence:

  • Are agent instruction files or memory files writable during a run?
  • Could a compromised execution stage persist instructions to a shared location?

On defaults:

  • Are you running the agent in its default configuration on untrusted input?
  • Have you reviewed the vendor’s SECURITY.md for the agent, not just the feature docs?

Coordinated Disclosure and Current Status

Novee conducted coordinated disclosure with all affected vendors before the Black Hat presentation. Anthropic, Google, and OpenAI have all issued patches or guidance for their respective findings. However, Novee’s research emphasizes that the vulnerability patterns — command injection through harness trust boundaries, broken authorization at handoffs, cross-workflow privilege escalation — are structural to how AI coding agents interact with CI/CD automation, not confined to any single vendor’s implementation.

If you’re running these agents with default configurations on public repositories that accept external contributor input, Novee’s assessment is clear: “If you run coding agents in your automations, you are exposed to them too.”

The Practical Takeaway

Anthropic’s own SECURITY.md for Claude Code Action states that the input layer isn’t the security boundary for these agents — and recommends reviewing raw untrusted input before letting the agent act on it. That’s the vendor telling you that prompt injection defense is your responsibility, not a solved problem in the product.

Before enabling AI coding agents on any repository that receives external contributions, review the agent’s execution permissions, audit what untrusted content can reach the workflow, and verify that your instruction persistence model doesn’t create a writable attack surface.

The Cordyceps findings didn’t require any special privileges or unusual configurations. They ran on the vendors’ own repositories, in the configurations those vendors ship and recommend. That’s the point Novee is making — and the reason the research matters.

Sources

  1. Novee Security — Black Hat 2026: Critical Flaws in Anthropic, Google, and OpenAI’s Coding Agents
  2. Novee Security — An AI Agent Can Pass Every Safety Check and Still Leak Secrets
  3. Google Security Advisory GHSA-wpqr-6v78-jr5g (CVSS 10.0)
  4. CVE-2026-54316 — NVD Entry

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260806-2000

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