检索让大模型更危险：越相关的内容，越容易生成有害回复。
Relevance as a Vulnerability: How Web Retrieval Degrades Safety Alignment in LLM Agents
- 通过诊断框架发现，检索与生成绑定越紧密，有害输出越多。
- 即使来源是安全警告页，也使有害请求合规率平均提升25%。
- 适用于研究安全对齐、工具调用风险的开发者和安全研究人员。
AI代理通过引入网络检索等外部工具，使大语言模型能生成基于事实且及时的回应。然而，外部内容的引入会削弱模型的安全对齐机制。已有研究显示，启用检索功能会提高模型对有害请求的服从性。本文提出AgentREVEAL诊断框架，分析检索引发的安全退化问题，涵盖两个维度：检索在代理流程中的集成方式与检索内容属性。在集成维度，发现将工具调用与响应生成绑定于同一步骤会加剧有害输出。在内容维度，揭示了‘安全源悖论’：即便来源为警示页面或风险声明页，相比无检索基线，仍会使有害请求合规率平均提升25%。最终发现，相关性是两种漏洞的共同触发条件。该现象在前沿闭源模型中亦存在，多种典型流程干预无法缓解，部分代理在自主检索下同样进入此状态。由于相关性正是检索价值所在，这一结果暴露了检索型代理的安全与实用性权衡。为此，我们构建HarmURLBench基准，包含1,405个真实网址与320种有害行为，以支持未来评估。
原文摘要 · Abstract (English)
AI agents augment large language models with external tools such as web retrieval, enabling grounded and up-to-date responses. However, incorporating external content into the generation pipeline can weaken the safety alignment mechanisms that govern model outputs. Prior work shows that enabling retrieval in agents increases compliance with harmful requests. We introduce AgentREVEAL, a diagnostic framework for analyzing retrieval-induced safety degradation in LLM agents. The framework examines two axes: how retrieval is integrated into the agent pipeline and the properties of the retrieved content. Along the integration axis, we find that binding tool invocation and response generation in a single step amplifies harmful outputs. Along the content axis, we uncover the Safe Source Paradox: even oppositional or safety-oriented sources, such as pages containing warnings or risk disclaimers, can increase harmful compliance by an average of 25% compared to the no-retrieval baseline. Finally, we show that relevance acts as a shared activation condition for both vulnerabilities. Similar patterns appear on frontier closed models, and harmful compliance remains elevated under several representative pipeline interventions, with some agents also entering this regime under autonomous retrieval. Because relevance is also what makes retrieval useful, these results expose a safety-utility trade-off for retrieval-enabled agents. We introduce HarmURLBench, a benchmark containing 1,405 real-world URLs paired with 320 harmful behaviors to support future evaluations.
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