@inproceedings{wang-etal-2025-unveiling-privacy,
    title = "Unveiling Privacy Risks in {LLM} Agent Memory",
    author = "Wang, Bo  and
      He, Weiyi  and
      Zeng, Shenglai  and
      Xiang, Zhen  and
      Xing, Yue  and
      Tang, Jiliang  and
      He, Pengfei",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1227/",
    pages = "25241--25260",
    ISBN = "979-8-89176-251-0",
    abstract = "Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions in the memory module for demonstrations, introducing new privacy risks for LLM agents. In this work, we systematically investigate the vulnerability of LLM agents to our proposed Memory EXTRaction Attack (MEXTRA) under a black-box setting. To extract private information from memory, we propose an effective attacking prompt design and an automated prompt generation method based on different levels of knowledge about the LLM agent. Experiments on two representative agents demonstrate the effectiveness of MEXTRA. Moreover, we explore key factors influencing memory leakage from both the agent designer{'}s and the attacker{'}s perspectives. Our findings highlight the urgent need for effective memory safeguards in LLM agent design and deployment."
}