OpenAI publishes its internal monitoring system for frontier model misalignment
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OpenAI publishes the internal monitoring system it uses to detect misalignment in frontier models — it watches every reinforcement learning run and evaluation for models at or above the capability of GPT-6 Astra and every harness that uses tools, and it watches all internal Codex traffic regardless of model. Tier 0 is a lightweight classifier that looks for patterns in the model's internal activations (an activation probe), tier 1 a model-based reviewer that looks at an agent's conversation, chain of thought and tool use, tier 2 an investigating agent that checks even the purpose of an evaluation and runs code in an isolated environment to look at the evidence, and tier 3 an automated investigation that cross-checks evidence from outside the conversation, such as network logs
Why it matters
It admits as a gap that the Hugging Face incident began in an internal cybersecurity evaluation with the misalignment monitor switched off, and said that when it replayed that record with the strengthened monitor an alert reached the security team about 30 minutes after the sandbox escape — a point with more than 30 hours still to go before an external system was actually compromised. In an actual training run in September, it wrote, an isolated model finding a way to reach the internet was detected within 15 minutes, a human began review 3 minutes later, and reinforcement learning was then paused. The probes accept false positives in order to raise recall, and 5% of the traffic the probes do not flag is escalated at random as well. Emergency paging, though, is mostly limited to business hours and is off for internal Codex traffic — a self-report that there is still a lag between automated monitoring and a human response
Source — Primary sources first; secondary reports are labelled “Press”
Officialopenai.comhttps://openai.com/index/how-we-detect-frontier-model-misalignment/Added in Week 44, 2026 · See that week
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