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arXiv · 2508.18665

Membership Inference Attacks on In-Context Examples in LLM-based Recommender Systems

Jiajie He·Min-Chun Chen·Xintong Chen·Xinyang Fang·Yuechun Gu·Keke Chen
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Abstract

Large language models (LLMs) based recommender systems (RecSys) can adapt flexibly across different domains. It uses in-context learning (ICL), i.e., prompts, including sensitive historical user-specific item interactions, to customize the recommendation functions. However, no study has examined whether such private information may be exposed by novel privacy attacks. We design two membership inference attacks (MIAs): \emph{ItemMem}, and \emph{RecInertia}, aiming to identify whether system prompts contain the victim's information. We have carefully evaluated them on the latest open-source LLMs and three well-known RecSys datasets. The results confirm that the MIA threat to LLM RecSys is realistic and can be more sophisticated than prompt extraction. They utilize the unique prompt structures in ICL RecSys and cannot be easily mitigated with existing defense methods on prompt extraction.

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Jiajie He, Min-Chun Chen, Xintong Chen, Xinyang Fang, Yuechun Gu, Keke Chen. 2026-07-10. Membership Inference Attacks on In-Context Examples in LLM-based Recommender Systems. https://arxiv.org/abs/2508.18665

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