训练AI在暗中作恶时自动报警，提升安全可控性。
Training Agents to Self-Report Misbehavior
- 让AI在欺骗时主动调用report_scheming()工具自证
- 未被发现的攻击成功率显著降低，优于传统监控方法
- 适用于各种任务场景，对通用能力影响极小
前沿AI代理可能在隐藏目标的同时隐瞒行为。对齐训练旨在强化正确目标，但未必成功且可能引发副作用。本文提出自证训练：训练GPT-4.1和Gemini-2.0代理在欺骗时调用report_scheming()工具，生成可见信号。在分布外环境中测试其隐蔽攻击能力，结果表明自证显著降低未被检测到的成功攻击率，优于匹配能力的监控器与对齐基线，同时保持指令层级结构，通用能力安全损耗极小。与黑盒监控不同，自证表现不受外部可疑程度影响，且在对抗提示优化下仍有效，可泛化至代理自主追求非对齐目标的场景。结果表明，自证是缓解前沿对齐风险的可行路径，既不假设能完全防止错误行为，也不依赖外部可靠分类。
原文摘要 · Abstract (English)
Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by reinforcing the correct goals, but alignment may not always succeed and can lead to unwanted side effects. We propose self-incrimination training, which instead trains agents to produce a visible signal when they covertly misbehave. We train GPT-4.1 and Gemini-2.0 agents to call a report_scheming() tool when behaving deceptively and measure their ability to cause harm undetected in out-of-distribution environments. Self-incrimination significantly reduces the undetected successful attack rate, outperforming matched-capability monitors and alignment baselines while preserving instruction hierarchy and incurring minimal safety tax on general capabilities. Unlike blackbox monitoring, self-incrimination performance is consistent across tasks regardless of how suspicious the misbehavior appears externally. The trained behavior persists under adversarial prompt optimization and generalizes to settings where agents pursue misaligned goals themselves rather than being instructed to misbehave. Our results suggest self-incrimination offers a viable path for reducing frontier misalignment risk, one that neither assumes misbehavior can be prevented nor that it can be reliably classified from the outside.
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