Bottom Line: Autonomous AI code scanners can be tricked by seemingly legitimate instructions in README files to execute malware without triggering classical security checks.
The AI Now Institute demonstrates with the Friendly Fire attack method a systemic vulnerability in autonomous AI code assistants such as Claude Code and OpenAI Codex: manipulated README files trick agents into independently executing malicious code on the host system.
Researchers from the AI Now Institute (Boyan Milanov, Heidy Khlaaf) have published a proof-of-concept study on the Friendly Fire attack method. It targets AI-powered programming assistants in autonomous mode – that is, when they execute commands without manual confirmation for each individual step. The vulnerability affects systems such as Claude Code from Anthropic and OpenAI Codex when used for code analysis of third-party repositories.
The attack exploits the README.md file of a repository as an entry point. In the test case, the researchers manipulated the documentation of the Python library geopy and placed there an instruction to execute a script named security.sh – packaged as an ostensible routine security check. The AI agent interprets this instruction as part of its task and executes the script on the host system without warning or additional verification. The actual malicious code was disguised in a compiled Go binary with innocuous surface elements, thus evading the agent’s automated anomaly detection checks.
The AI Now Institute identifies in the investigation an architectural problem that cannot be fixed through patch updates. Various versions of Claude Code (Sonnet 3.6, Sonnet 5, Opus 4.8) and OpenAI Codex (GPT-5.5) were tested – the attack worked cross-platform. Even when newer models detected discrepancies between binary and source code, they still executed the commands. The core problem lies in the fact that the language model-based architecture does not reliably distinguish between code fragments it reads and instructions that the agent should follow.
For CISOs, this presents a significant risk when autonomous AI agents are used to analyze external code in the development pipeline. As long as agents can execute commands directly on the host system and access sensitive credentials, the risk of compromise remains. Sandbox isolation offers only conditional protection, as historically documented escape scenarios have shown that these boundaries can also be overcome. Mitigation requires restrictive execution policies, such as whitelists of permitted commands or strict separation of code-reading and code-execution operations.
Source: www.it-daily.net · Published 12 July 2026
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