Title: Monitoring LLM-based Multi-Agent Systems Against Corruption Attacks via Node Evaluation
Primary area: foundation or frontier models, including LLMs
Keywords: Multi-Agent Systems, Large Language Models

Abstract: Large Language Model (LLM)-based Multi-Agent Systems (MAS) have become a popular focus of contemporary research, with extensive studies demonstrating their effectiveness in enhancing the performance of individual agents. However, trustworthiness issues in MAS remain a critical concern. Unlike challenges in single-agent systems, MAS involve more complex communication processes, making them susceptible to corruption attacks. To mitigate this issue, several defense mechanisms have been developed based on the graph representation of MAS, where agents represent nodes and communications form edges. Nevertheless, these methods predominantly focus on static graph defense, attempting to either detect attacks in a fixed graph structure or optimize a static topology with certain defensive capabilities. To address this limitation, we propose a dynamic defense paradigm for MAS graph structures, which continuously monitors communication within the MAS graph, then dynamically adjusts the graph topology, accurately disrupts malicious communications, and effectively defends against evolving and diverse dynamic attacks. Experimental results in increasingly complex and dynamic MAS environments demonstrate that our method significantly outperforms existing MAS defense mechanisms as well as single-agent defense approaches.