Misinformation Propagation in Benign Multi-Agent Systems
TL;DR
This work studies the benign case: agents follow one protocol, none intends to deceive, but some read a misleading passage, testing three open-weight models on commonsense reasoning, knowledge QA, and moral judgment, and concludes that robustness should be judged against exposure-matched baselines.
Abstract
Multi-agent systems (MAS) are credited with solving complex problems, yet their agents routinely act on context that may be false. Prior work shows single LLMs are swayed by false context, and MAS can be compromised by adversarial agents persuading peers. We study the benign case: agents follow one protocol, none intends to deceive, but some read a misleading passage. We study it with MINT, 10,278 LLM-written misinformation passages in eight styles, testing three open-weight models on commonsense reasoning, knowledge QA, and moral judgment. A relevant false passage costs a single agent 0.12-0.43 accuracy; irrelevant or content-free passages typically cost at most 0.06. Debate looks robust because uninformed agents outnumber misinformed ones. With one of three misinformed, it is no better than choosing the majority over equally exposed independent samples. Interaction helps only with a misinformed majority, recovering up to 0.24 accuracy over voting (0.29 under consensus). A one-sentence unreliability warning recovers up to 0.18 accuracy under voting (0.28 under consensus), at no significant cost under voting when context is true. Robustness should be judged against exposure-matched baselines, with prompt-level skepticism as cheap first defense.
