Year: 2025
Type: conference-paper
Abstract: Large Language Model (LLM) agents have become increasingly prevalent across various realworld applications.They enhance decisionmaking 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.
Institutions: Michigan State University, University of Georgia
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: Interactive, Individualized Professional Learning for Elementary School Teachers: Enhancing Content and Pedagogical Content Knowledge as a Basis for Improving Practice, NeTS: Small: Exploring the Non-Standardized Polices, Operations, and Requirements for 5G Cellular Networks and Beyond: Advancing the Modeling, Tools, and Evaluation, III:Medium:Computation and Communication Efficient Distributed Learning, Collaborative Research: SaTC: CORE: Medium: Safeguarding Next-Generation Emergency Services (NG-9-1-1) over Cellular Networks: From Design to Practice, Intelligent, Adaptive Program with Just-in-time Feedback for Preservice Teachers, Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications, TRTech-PGR: Maize Hub: An Open-source Simulation Platform & Resources Integrating Extensive Field Trial Data, DNA Sequences, and Historical Weather Records, TRTech-PGR: Connecting sequences to functions within and between species through computational modeling and experimental studies
OpenAlex ID: W4412889952