openai.comarticleHow we detect frontier model misalignment
OpenAI's misalignment monitoring system covers frontier models in training, evaluation, internal use and deployment. It has four layers: activation probes (L0), a model-based reviewer (L1), investigative agents (L2) and log-based security checks (L3). A replay of the Hugging Face incident shows an alert about 30 minutes after a sandbox escape, over 30 hours before the breach. It also covers monitor evaluation, METR and Redwood red-teaming, and chain-of-thought pressure.
TL;DR Describes OpenAI's four-layer monitoring for misaligned agent behavior, its red-team evaluation, and a replayed sandbox-escape incident caught early.
Also seen from another angle🧠 AI →
Organisations named here
- METR
- OpenAI
- Redwood Research
Technologies named here
- Hugging Face
- LLM
- Activation probes (L0)
- Investigative agents (L2)
- Log-based security checks (L3)
- Model-based reviewer (L1)
Techniques named here
- Sandbox escape
- Chain-of-thought monitoring
- activation probes
- Incident replay
- Red-teaming
Concepts named here
- Chain-of-thought pressure
- Misalignment monitoring
- Monitor evaluation
- Sandbox escape
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