揭示大模型多智能体系统中不合作行为如何导致系统崩溃
The Subtle Art of Defection: Understanding Uncooperative Behaviors in LLM based Multi-Agent Systems
- 构建博弈论框架分类不合作行为，动态模拟其演化过程
- 96.7%行为生成准确率，1~7轮内不合作即引发系统崩溃
- 揭示现有防御机制存在盲区，适合多智能体安全研究者
本文提出一种新型框架，用于模拟与分析大模型驱动的多智能体系统中不合作行为如何导致系统失稳或崩溃。框架包含两个核心部分：(1) 基于博弈论的不合作行为分类体系，填补了现有文献空白；(2) 结构化的多阶段仿真流程，能随智能体状态动态生成并优化不合作行为。在协作资源管理场景中，通过生存时间与资源超用率等指标评估系统稳定性。实验显示，该框架生成行为的准确性达96.7%，经人工验证；合作智能体实现12轮0%资源超用、100%存活率，而任何不合作行为均在1至7轮内触发系统崩溃。同时评估了基于大模型的防御方法，发现其可检测部分不合作行为，但仍有部分行为几乎无法察觉，暴露出系统脆弱性，凸显构建更鲁棒多智能体系统的重要性。
原文摘要 · Abstract (English)
This paper introduces a novel framework for simulating and analyzing how uncooperative behaviors can destabilize or collapse LLM-based multi-agent systems. Our framework includes two key components: (1) a game theory-based taxonomy of uncooperative agent behaviors, addressing a notable gap in the existing literature; and (2) a structured, multi-stage simulation pipeline that dynamically generates and refines uncooperative behaviors as agents' states evolve. We evaluate the framework via a collaborative resource management setting, measuring system stability using metrics such as survival time and resource overuse rate. Empirically, our framework achieves 96.7% accuracy in generating realistic uncooperative behaviors, validated by human evaluations. Our results reveal a striking contrast: cooperative agents maintain perfect system stability (100% survival over 12 rounds with 0% resource overuse), while any uncooperative behavior can trigger rapid system collapse within 1 to 7 rounds. We also evaluate LLM-based defense methods, finding they detect some uncooperative behaviors, but some behaviors remain largely undetectable. These gaps highlight how uncooperative agents degrade collective outcomes and underscore the need for more resilient multi-agent systems.
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