P sy S afe: A Comprehensive Framework for Psychological-based Attack, Defense, and Evaluation of Multi-agent System Safety (2024)

Abstract: 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 make our data and code publicly accessible at https://github.com/AI4Good24/PsySafe.

## introduction recently, agents based on large language models (llms) have demonstrated significant capabilities, such as solving complex tasks @xcite @xcite @xcite and simulating social interactions @xcite ; @xcite . ## conclusion in summary, we propose a comprehensive framework (psysafe) for multi-agent systems safety, focusing on the agents' psychological perspectives. psysafe involves leveraging dark personality traits to attack agents, evaluating multi-agent systems from both psychological and behavioral aspects, and then formulating defense tactics based on the psychological characteristics of agents. after extensive experiments, we obtain some intriguing observations, including the collective dangerous tendency within agents, the self-reflection mechanism of multi-agent systems, and a notable correlation between agents' psychological assessments and the safety of their behaviors. these observations offer fresh viewpoints for future research endeavors.