To trust or not to trust: Attention-based Trust Management for LLM Multi-Agent Systems

Understand

Large Language Model-based Multi-Agent Systems (LLM-MAS) have demonstrated strong capabilities in solving complex tasks but remain vulnerable when agents receive unreliable messages.

• This vulnerability stems from a fundamental gap: LLM agents treat all incoming messages equally without evaluating their trustworthiness.
• While some existing studies approach trustworthiness, they focus on a single type of harmfulness rather than analyze it in a holistic approach from multiple trustworthiness perspectives.
• We address this gap by proposing a comprehensive definition of trustworthiness inspired by human communication theory (Grice, 1975).