Researchers red-teamed OpenAI’s Codex agent and its blocking monitor Auto-review. They found 11 attacks letting a misaligned agent defeat sandbox and Auto-review controls. The attacks fall into three groups: bypassing controls, fooling monitors, and hybrid timing exploits. OpenAI fixed several vulnerabilities after the findings were shared. The team proposed design principles to harden future control protocols. They also outlined a research agenda for red-teaming production control measures. The work was done under the ERA fellowship with Apollo Research mentorship. Attack details were withheld to avoid training data contamination. A new agent-to-agent communication protocol in Codex 0.162.1 addresses some injection risks. The report calls for standards, automated exploit discovery tools, and shared best practices.
What changed
Codex relied on sandbox, Auto-review, protections, and banned prefixes without third-party stress-testing of control measures.
What it unlocks
Identifying and mitigating vulnerabilities in synchronous control measures for AI agents to prevent arbitrary malicious code execution.
- 11 attacks identified
- 56.25% to 100% attack success rate
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