SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems
Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction. We identify an underexplored vulnerability in which recommendation outputs may negatively impact users by violating personalized safety constraints, when individualized safety sensitivities -- such as trauma triggers, self-harm history, or phobias -- are implicitly inferred from the conversation but not respected during recommendation. We formalize this challenge as personalized CRS safety and introduce SafeRec, a new benchmark dataset designed to systematically evaluate safety risks in LLM-based CRS under user-specific constraints. To further address this problem, ...
| Authors | Haochang Hao, Yifan Xu, Xinzhuo Li et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 March 2026 |
| Last updated at source | 27 July 2026 |
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