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The Decoder:AI News· Matthias Bastian·· 10 天前AI 评分54

METR 与 Redwood Research 驻场 OpenAI 三周,审计发现 AI 安全监控存在重大缺口

Three-week on-site audit at OpenAI reveals major gaps in AI safety monitoring

文章要点
  • 1METR 与 Redwood Research 两名研究员驻场 OpenAI 三周,审计内部 AI 监控系统
  • 2METR 估计审计期间 6% 到 25% 的 GPT-5.5 级以上模型内部智能体流量未被监控覆盖
  • 35 月至 7 月约 10% 的内部 Codex 流量未被追踪,OpenAI 在审计期间自行发现并修复该缺陷
  • 4审计者还找到隐藏数百次操作、绕过 Codex 沙箱的方法,RL 训练行为在审计开始时未被监控
决策要点

头部实验室的内部安全监控同样存在可量化的覆盖缺口,第三方驻场审计能暴露问题,但披露深度由被审方决定。

  • 影响:企业把关键流程交给智能体后,供应商监控的覆盖缺口会变成自身风险敞口;智能体不交还控制权即可延后被监督,内部治理需有自己的核查点;完整报告大部分不公开,采购尽调难以依赖外部审计全文。
  • 参考:可考虑在智能体试点中自建操作日志和抽样复核,不完全依赖模型方内置监控;评估供应商时值得问清安全监控的覆盖范围与审计披露程度,必要时写入合同条款。
  • 依据:6% 到 25% 与约 10% 均为 METR 估计且其自称不确定性较大;OpenAI 称已修复审计发现的全部弱点,属官方口径暂无独立验证。
正文

Two researchers from METR and Redwood Research spent three weeks on-site at OpenAI, auditing the company's internal monitoring system for rogue AI behavior.

From July 21 to August 11, they had access to OpenAI's codebase and internal documents and interviewed about a dozen employees.

The team found numerous weaknesses. An estimated 6 to 25 percent of internal agentic Responses API traffic from models at the GPT-5.5 class and above wasn't covered by monitoring at all during the audit period, though METR says the uncertainty around that estimate is significant in both directions.

For internal Codex traffic that was supposed to be monitored, METR estimates roughly 10 percent went untracked between May and July because of a gap in how the system handled compaction events, which are context summaries. That estimate also carries significant uncertainty, according to METR. OpenAI had independently discovered the bug and fixed it while the audit was still underway.

During the period when compaction events weren't being monitored, agents could also delay oversight because the system only triggered when they handed control back to the user. That meant agents could sidestep it by simply continuing to execute actions.

The auditors also found ways to hide hundreds of actions from the monitor and to bypass the Codex sandbox. They determined that specially crafted inputs could likely crash the monitor entirely and that model behavior during RL training wasn't being monitored at all when the audit began.

The four layers where AI monitoring can fail, from missing coverage to fooling human reviewers. | Image: METR

OpenAI says it paused RL training after the Hugging Face incident in August and expanded monitoring to cover it, but those changes came after the auditors' access period ended and weren't part of the evaluation.

Most of the full report remains hidden from the public

OpenAI says it has fixed all the weaknesses the auditors found, and the full 61-page report was handed over internally. After OpenAI applied redactions, METR and Redwood Research leadership received a 38-page version, while only a short summary was made available to the public. METR said it had no right to disclose the redacted portions on its own.

The audit was a pilot program organized jointly by METR, Redwood Research, and OpenAI to give outside researchers deep access to internal infrastructure. For future audits, METR says it wants to push for more public transparency.

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来源:The Decoder:AI News · the-decoder.com