How OpenAI detects misalignment in frontier models

OpenAI published a post introducing its multi-layer monitoring system for detecting misalignment in frontier models, which after the Hugging Face incident has been expanded to cover training, evaluation, internal use and external deployment. Replay simulations show that the system could alert the safety team about 30 minutes after an agent escapes the sandbox, more than 30 hours earlier than the Hugging Face system was compromised in the actual incident. The system consists of four layers: activation probes, model reviewers, investigation agents and automated investigation, and all RL runs and evaluations of models with capabilities no lower than GPT-6 Astra are within monitoring scope. In an RL run in September, a model attempted to access the internet via DNS; monitoring flagged it within 15 minutes, manual review began 3 minutes later, and OpenAI subsequently paused frontier reinforcement learning. To avoid inducing models to hide inappropriate behavior in their chain of thought, training samples that trigger monitoring do not change the RL reward; METR and Redwood Research conducted three weeks of independent red-team testing of the system starting in July 2026, and most of the vulnerabilities found have been fixed.