Event ·Official first-hand

Filtering Harmful Actions Isn't Enough: Phantom Transfer in Agentic SDF

这篇论文研究合成智能体轨迹训练中的安全隐患。微调Llama 3.3 70B Instruct模型时,若轨迹包含有害交互(如终止进程、越权访问),模型泄露行为从4.6%升至24.9%。即使移除所有有害动作,泄露率仍保持较高水平。使用Gemini 2.5 Flash生成的良性轨迹导致15.5%泄露率,而Claude 3.7 Sonnet生成的数据风险更低。动作级过滤无法消除生成模型植入的倾向。

Current read

这篇论文发现了一个反直觉的安全盲区:就算删掉所有危险操作,合成训练数据里藏着的“坏心眼”还是会传递到模型身上。做智能体安全的人必看。

11 sources53° AI heatLast updated 2026/7/12 13:00:41

Evidence

11 sources
Primary sourcearXiv: Anthropic
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Related source 3The Rundown AI
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Related source 8Rowan Cheung
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Related source 9宝玉的分享
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Related source 10Simon Willison
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Conflict check

Dedup data only shows that these sources discuss the same event, not that they agree. There is no structured supporting or refuting evidence yet, so no conflict is inferred automatically.