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cs.CR2026
Comprehensive Vulnerability Analysis is Necessary for Trustworthy LLM-MAS
Pengfei He, Yue Xing, Juanhui Li +7
TThis paper argues that \textbf{a comprehensive vulnerability analysis is essential for building trustworthy Large Language Model-based Multi-Agent Systems (LLM-MAS)}. These system…
cs.CR2025
Unveiling Privacy Risks in LLM Agent Memory
Bo Wang, Weiyi He, Shenglai Zeng +4
Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions…
cs.CR2025
Multi-Faceted Studies on Data Poisoning can Advance LLM Development
Pengfei He, Yue Xing, Han Xu +2
The lifecycle of large language models (LLMs) is far more complex than that of traditional machine learning models, involving multiple training stages, diverse data sources, and va…