Unveiling Privacy Risks in LLM Agent Memory
Bo Wang, Weiyi He, Shenglai Zeng, Zhen Xiang, Yue Xing, Jiliang Tang, Pengfei He
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.
BibTeX
@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://aclanthology.org/2025.acl-long.1227/",
doi = "10.18653/v1/2025.acl-long.1227",
pages = "25241--25260",
ISBN = "979-8-89176-251-0"
}