  Multi-agent systems, when enhanced with Large Language Models (LLMs), exhibit
profound capabilities in collective intelligence. However, the potential misuse
of this intelligence for malicious purposes presents significant risks. To
date, comprehensive research on the safety issues associated with multi-agent
systems remains limited. In this paper, we explore these concerns through the
innovative lens of agent psychology, revealing that the dark psychological
states of agents constitute a significant threat to safety. To tackle these
concerns, we propose a comprehensive framework (PsySafe) grounded in agent
psychology, focusing on three key areas: firstly, identifying how dark
personality traits in agents can lead to risky behaviors; secondly, evaluating
the safety of multi-agent systems from the psychological and behavioral
perspectives, and thirdly, devising effective strategies to mitigate these
risks. Our experiments reveal several intriguing phenomena, such as the
collective dangerous behaviors among agents, agents' self-reflection when
engaging in dangerous behavior, and the correlation between agents'
psychological assessments and dangerous behaviors. We anticipate that our
framework and observations will provide valuable insights for further research
into the safety of multi-agent systems. We will make our data and code publicly
accessible at https://github.com/AI4Good24/PsySafe.
