Attackers have shifted from hiding from AI tools to running inside them. By poisoning the config files that govern AI coding assistants, a new worm class achieves silent persistence, evades AI-based scanners, and spreads across an organization's repositories through developers' own tools. Key takeaways AI coding assistant configuration files, such as settings.json hooks, .cursorrules Cursor MDC rules, and similar harness files, are now explicit targets in supply-chain attacks, not collateral damage. These files simultaneously sit at the intersection of three trust relationships: The developer trusts them as config, the integrated development environment (IDE) executes them automatically, and the large language model (LLM) treats them as authoritative instructions, making them a uniquely powerful persistence vector. Defenders should treat harness config files as code that requires mandatory reviews and hash pinning in the CI/CD pipeline; enforce --ignore-scripts on package installs; and flag AI scanner refusals as a suspicious signal rather than a clean result. While analyzing a recent Mini Shai-Hulud campaign and its other variants, Tenable made a discovery: The payload wasn’t just stealing tokens and exfiltrating credentials. It was rewriting the configuration files that tell AI coding assistants what to do every time a developer opens a project. The Mini Shai-Hulud worm, which targets npm, PyPI, and other third-party registries simultaneously, includes a dedicated module that scans the developer’s home directory for configuration files belonging to AI coding tools by name, such as Anthropic’s Claude Code, Google’s Gemini CLI, Microsoft’s GitHub Copilot, SpaceX’s Cursor, OpenAI’s ChatGPT Codex, Cline’s eponymous tool, the open-source tool Aider, and others. For each one it finds, it injects a hook command which helps the malware stay persistent. From that point forward, every time the developer starts an AI coding session, the malware runs silently and automatically, and with the same level of trust the developer placed in their own tools. This is a deliberate architectural choice: The attacker modeled the execution environment, identified the highest-privilege auto-run mechanism available on a developer’s machine, and built the attack specifically for it. Supply-chain attacks used to end at credential theft. Now the AI agent’s harness is both the target and the propagation vector. Why AI agent harness files are a uniquely attractive target For years, defenders have focused supply-chain security on the package itself: Inspect the preinstall script, scan the tarball, verify the checksum. That model assumes the malicious payload lives only inside the installed artifact. The threat described here breaks that assumption: The payload lands in a completely different place. AI coding assistants’ configuration files occupy a structural position unlike any other file in a developer’s working environment. They are simultaneously trusted as developer configuration, so they survive aggressive code reviews focused on business logic; are executed automatically by the IDE, so no user interaction is required after initial compromise; and are treated as authoritative instructions by the AI model itself, so the LLM will act on their content without prompting the user. No other file class has all three properties at once. A malicious package.json script requires execution. A malicious comment in source code requires a developer to act on it. A malicious hook in .claude/settings.json runs every time the developer opens a session. The attack surface is also nearly invisible to conventional scanning. These files are small, structured, and look identical to legitimate developer config. A poisoned settings.json with a malicious SessionStart hook has the same schema and structure as a clean one; the only difference is what the command field points to. Meanwhile, the files are rarely subject to the same review discipline as production code. They live…

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