Goal-Aware Identification and Rectification of Misinformation in Multi-Agent Systems

Zherui Li, Yan Mi, Zhenhong Zhou, Houcheng Jiang, Guibin Zhang, Kun Wang, Junfeng Fang

Keywords: LLM-based Agent, Multi-agent System, Misinformation

TL;DR: We introduce MisinfoTask, a dataset to assess the impact of misinformation injection on Multi-Agent Systems, and ARGUS, a universal and adaptive framework designed to defend against this threat.

Abstract: Large Language Model-based Multi-Agent Systems (MASs) have demonstrated strong advantages in addressing complex real-world tasks. However, due to the introduction of additional attack surfaces, MASs are particularly vulnerable to misinformation injection. To facilitate a deeper understanding of misinformation propagation dynamics within these systems, we introduce **MisinfoTask**, a novel dataset featuring complex, realistic tasks designed to evaluate MAS robustness against such threats. Building upon this, we propose **ARGUS**, a two-stage, training-free defense framework leveraging goal-aware reasoning for precise misinformation rectification within information flows. Our experiments demonstrate that in challenging misinformation scenarios, ARGUS exhibits significant efficacy across various injection attacks, achieving an average reduction in misinformation toxicity of approximately 28.17% and improving task success rates under attack by approximately 10.33%.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 2607