When Personalization Legitimizes Risks: Uncovering Safety Vulnerabilities in Personalized Dialogue Agents
Jiahe Guo, Xiangran Guo, Yulin Hu, Zimo Long, Xingyu Sui Harbin Institute of Technology, Xuda Zhi, Yongbo Huang, Hao He 等 11 位作者
Long-term memory enables large language model (LLM) agents to support personalized and sustained interactions.However, most work on personalized agents prioritizes utility and user experience, treating memory as a neutral component and largely overlooking its safety implications.In this paper, we reveal intent legitimation, a previously underexplored safety failure in personalized agents, where benign personal memories bias intent inference and cause models to legitimize inherently harmful queries.To study this phenomenon, we introduce PS-Bench, a benchmark designed to identify and quantify intent legitimation in personalized interactions.Across multiple memory-augmented agent frameworks and base LLMs, personalization increases attack success rates by 15.8%–243.7% relative to stateless baselines.We further provide mechanistic evidence for intent legitimation from internal representation space, and propose a lightweight detection–reflection method that effectively reduces safety degradation.Overall, our work provides the first systematic exploration and evaluation of intent legitimation as a safety failure mode that naturally arises from benign, real-world personalization, highlighting the importance of assessing safety under long-term personal context. WARNING: This paper may contain harmful content.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表