OpenAI 在 Hugging Face 事件后扩展前沿模型失准监测
OpenAI 在 Hugging Face 事件后，将失准监测扩展到 GPT-6 Astra 级模型的 RL 训练、评测和工具使用配置，以及 Codex 内部流量和客户对 Astra 级网络安全模型的使用。
After the Hugging Face breach, OpenAI extended misalignment monitoring across frontier model training and deployment.
OpenAI has revealed the 4-layer monitoring system it uses to catch frontier AI agents misbehaving.
Says the Hugging Face breach began in internal cybersecurity evaluations where OpenAI ran no misalignment monitors.
> Before, OpenAI’s misalignment monitors mainly watched its internal coding agents, so the Hugging Face breach began in cyber evaluations that nobody was monitoring for misbehavior.
> Now the monitors cover every RL run, evaluation and tool-use setup for GPT-6 Astra-class models, all internal Codex traffic, and customer tool use of Astra-class cyber models.
> Activity is screened in up to 4 layers, from cheap activation probes to an AI reviewer to investigator agents checking network logs, and top alerts typically page humans within 15 minutes.
> OpenAI says this setup, replayed on the Hugging Face incident, would have alerted security about 30 minutes after the sandbox escape and more than 30 hours before Hugging Face was breached.
来源：rohanpaul_ai · x.com