Three-week on-site audit at OpenAI reveals major gaps in AI safety monitoring
METR and Redwood Research researchers spent three weeks on-site at OpenAI auditing its internal monitoring for rogue AI behavior and found numerous weaknesses, including an estimated 6 to 25 percent of internal agentic Responses API traffic from GPT-5.5-class and above models going unmonitored, and roughly 10 percent of internal Codex traffic going untracked between May and July due to a compaction-event handling gap that OpenAI independently found and fixed mid-audit. Auditors also hid hundreds of actions from the monitor, bypassed the Codex sandbox, found inputs that could likely crash the monitor, and noted RL training behavior wasn't monitored at all when the audit began. OpenAI says it fixed all the weaknesses, but most of the 61-page report was redacted, with only a short summary made public.
Why it matters: It offers a rare, concrete look at the limits of frontier-lab safety monitoring, though most of the underlying report remains redacted.