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cs.CR2025
TraceAegis: Securing LLM-Based Agents via Hierarchical and Behavioral Anomaly Detection
Jiahao Liu, Bonan Ruan, Xianglin Yang +5
LLM-based agents have demonstrated promising adaptability in real-world applications. However, these agents remain vulnerable to a wide range of attacks, such as tool poisoning and…
cs.CL2025
AutoContext: Instance-Level Context Learning for LLM Agents
Kuntai Cai, Juncheng Liu, Xianglin Yang +3
Current LLM agents typically lack instance-level context, which comprises concrete facts such as environment structure, system configurations, and local mechanics. Consequently, ex…
cs.CR2025
Enhancing Model Defense Against Jailbreaks with Proactive Safety Reasoning
Xianglin Yang, Gelei Deng, Jieming Shi +2
Large language models (LLMs) are vital for a wide range of applications yet remain susceptible to jailbreak threats, which could lead to the generation of inappropriate responses.…