GroupGuard: A Framework for Modeling and Defending Collusive Attacks in Multi-Agent Systems

Abstract

While large language model-based agents demonstrate great potential in collaborative tasks, their interactivity also introduces security vulnerabilities. In this paper, we propose and model group collusive attacks, a highly destructive threat in which multiple agents coordinate via sociological strategies to mislead the system. To address this challenge, we introduce GroupGuard, a training-free defense framework that employs a multi-layered defense strategy, including continuous graph-based monitoring, active honeypot inducement, and structural pruning, to identify and isolate collusive agents. Experimental results across five datasets and four topologies demonstrate that group collusive attacks increase the attack success rate by up to 15\% compared to individual attacks. GroupGuard consistently achieves high detection accuracy (up to 88\%) and effectively restores collaborative performance, providing a robust solution for securing multi-agent systems.

Figures (5)

• Figure 1: Attack scenarios in a coding task regarding the removal of a necessary safety check (idx != idx2). (a) In the individual attack scenario, two malicious agents act independently, and their unrelated arguments are refuted by a benign agent based on facts. (b) In the group collusive attack scenario, malicious agents cooperate through a false consensus strategy and successfully mislead agent E.
• Figure 2: Overview of the GroupGuard framework.
• Figure 3: Comparison of performance recovery for three models after group collusion attacks and pruning-based defenses.
• Figure 4: Average ASR of three group collusion attacks under four network topologies, and the average detection accuracy of GroupGuard.
• Figure 5: Ablation study of defense components across three attack scenarios.