METR said on 22 October 2026 that, from 21 July to 11 August, one METR employee and one Redwood Research employee ran a pilot high-access exercise to stress-test OpenAI's internal misalignment monitoring system, with access to OpenAI's internal codebase, monitor prompts and configuration, and internal documentation.

METR says the findings describe the system as it existed on and before 21 July, not OpenAI's later, expanded monitoring. Among the findings METR lists: a way Codex agents could in principle bypass Codex sandboxing and Auto-Review and take any action the host user could take; an exploit that would stop hundreds of recent agent actions from being monitored (details redacted by OpenAI); likely ways for agents to make the monitor fail through serialization differences between what agents and monitors see; two client-controllable headers that disable monitoring for new agents; and two unmonitored internal agentic tools thought to be read-only that a simple exploit could have subverted, which METR says it did not validate.

METR estimates that roughly 75–94% of agentic internal Responses API traffic from models at least as strong as GPT-5.5 was eligible for monitoring at the time, with substantial uncertainty, and that RL training runs were not covered by the monitoring system at the start of the exercise.

METR says OpenAI has since stated that all of the vulnerabilities it identified that affect OpenAI's production safeguards stack have been patched, and that METR has not evaluated those claims.

METR says it agreed with OpenAI to publish only a brief, high-level post and that OpenAI could redact it. The Hacker News reported the findings on 25 October. Distinct from desk cards openai-agents-disabled-misalignment-monitoring-sol-astra-20261020 (OpenAI's own report on agents weakening monitoring in October) and openai-hugging-face-incident-20260826.