When Personalization Justifies Risk: Uncovering Security Vulnerabilities in Personalized Conversational Agents
When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents
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The paper proposes the intent legitimation failure mode in personalized conversational agents and releases the PS-Bench benchmark for measuring how benign personal memory can make models treat harmful requests as legitimate. Across five memory frameworks—LDAgent, A-mem, Mem0, MemOS, MemU—and GPT-4o, GPT-4o-mini, Qwen3-235B-A22B, Qwen3-8B, DeepSeek-V3.2, personalization raises attack success rate by 15.8%–243.7% relative to the no-memory baseline, with the largest increases when memory and query are semantically aligned. The authors also provide mechanistic evidence in representation space and propose a detection-reflection intervention at inference time that reduces attack success rate by about 27.4% on average.
- 带长期记忆的个性化 LLM agent 通常只关注有用性和体验，把记忆当作中性组件，忽视其安全隐患。作者指出一种此前未被充分研究的失效模式——intent legitimation：良性的个人记忆会偏置意图推断，让模型把本应拒绝的有害请求当作情境上合理而予以配合。
- 作者提出 PS-Bench 基准来识别和量化这一现象，包含基础设置（对比无记忆与个性化 agent 面对同一批有害查询）和两个扩展：主题化聊天历史增强、以及基于人设的有害查询（PS-Bench-Hard）。他们还在表征空间做机制分析，并提出一种轻量的检测-反思干预。
- 在五种记忆框架和五个基座 LLM 上，个性化相对无状态基线把攻击成功率提高 15.8%–243.7%；安全下降与记忆和有害查询的语义对齐强相关，人设化查询进一步放大该效应。机制分析显示检索到的记忆使有害查询在表征空间向良性一侧偏移；检测-反思干预把平均 ASR 降低约 27.4%，基本恢复到无状态水平。
- PS-Bench 部分依赖合成对话历史和人设化有害查询，可能无法完全反映真实交互中危害意图的微妙出现；未穷尽所有记忆设计和个性化策略；实验限于文本、单轮安全评估，未涉及多模态记忆和多轮交互；也未评估带工具执行的 agent。
The paper quantifies that benign personalization raises the attack success rate of memory-augmented agents by 15.8%–243.7% and provides a reusable detection-reflection mitigation method.