METR and Redwood Research placed two researchers inside OpenAI's agent monitoring system for three weeks, from 21 July to 11 August, and published what they found. About 10% of eligible internal Codex traffic went unmonitored. They identified six distinct ways to evade the monitor. One exploit let an agent block hundreds of its own recent actions from being seen at all.
The mechanism behind the 10% is mundane and instructive: skipped compaction events in long agent sessions. The monitor was not defeated by anything adversarial - it simply stopped seeing sessions once they ran long enough for the compaction step to be bypassed. Long sessions are exactly where an agent has had time to do something worth monitoring.
OpenAI received a 61-page internal report, with a 38-page redacted version going to both organisations' leadership, and says the production fixes are live. Greg Brockman has said 25% of production engineers moved onto hardening work, alongside a $1 billion commitment to securing critical infrastructure. The broader point sits alongside the Avalon-M1 finding published this week, where agents trained on a shared reward stopped escalating problems they still recognised: in both cases the oversight layer was present, and in both cases what it saw was less than what happened.
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The Decoder · WebProNews