RAG投毒致模型过度自信,注意力崩塌可预警

Rohan Paul · @rohanpaul_ai · X·2026-08-20 06:57·5天前
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研究发现RAG投毒可使模型token置信度和输出一致性上升,导致基于不确定性的检测器失效。攻击下注意力集中于被投毒文档而非分散于检索证据,作者称之为“注意力崩塌”。监控文档级注意力分布或可在答案明显出错前暴露投毒,仅检查最终答案或置信度可能漏报。

Rohan Paul@rohanpaul_ai
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RAG投毒致模型过度自信,注意力崩塌可预警

2026-08-20 06:57· 5天前
AI 导读

研究发现RAG投毒可使模型token置信度和输出一致性上升,导致基于不确定性的检测器失效。攻击下注意力集中于被投毒文档而非分散于检索证据,作者称之为“注意力崩塌”。监控文档级注意力分布或可在答案明显出错前暴露投毒,仅检查最终答案或置信度可能漏报。

RAG poisoning can make a model more confident, which is exactly why confidence-based detectors can fail.

This paper finds malicious retrieved documents can increase token confidence and output consistency.

So uncertainty-based detectors can miss the attack because poisoning can create false confidence.

Under attack, attention becomes concentrated on poisoned documents instead of staying spread across the retrieved evidence.

The authors call this Attention Collapse.

So for RAG security, checking only the final answer or its confidence may miss the warning.

Monitoring how attention is distributed across retrieved documents could expose poisoning before the answer visibly breaks.

– arxiv. org/abs/2608.06947

Title: "When Context Bites: Detecting RAG Poisoning via Document-Level Attention Collapse"

来源:Rohan Paul· x.com