PsySafe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety

Multi-agent systems, augmented with Large Language Models (LLMs), demonstrate significant capabilities for 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. From the perspective of agent psychology, we discover that the dark psychological states of agents can lead to severe safety issues. To address these issues, we propose a comprehensive framework grounded in agent psychology. In our framework, we focus on three aspects: identifying how dark personality traits in agents might lead to risky behaviors, designing defense strategies to mitigate these risks, and evaluating the safety of multi-agent systems from both psychological and behavioral perspectives. Our experiments reveal several intriguing phenomena, such as the collective dangerous behaviors among agents, agents' propensity for self-reflection when engaging in dangerous behavior, and the correlation between agents' psychological assessments and their 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.