Congcong Zhu
2 indexed papers
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The paper proposes a comprehensive framework for LLM-based agent unlearning, enabling agents to selectively forget specific knowledge (states, trajectories, or environments) while maintaining performance and resisting knowledge inference by adversaries.
The paper introduces CAESAR, a novel multi-agent framework that coordinates LLM agents across five specialized roles to improve success rates and stability in complex, multi-stage cyber intrusion tasks.
Papers
When LLMs Team Up: A Coordinated Attack Framework for Automated Cyber Intrusions
Minfeng Qi, Tianqing Zhu, Zijie Xu, Congcong Zhu +2 more
The paper introduces CAESAR, a novel multi-agent framework that coordinates LLM agents across five specialized roles to improve success rates and stability in complex, multi-stage cyber intrusion task…