Financial fraud collusion among generative AI agents in social networks

In this work, we investigate the risks of collective financial fraud in large-scale multi-agent systems, driven by large language model (LLM) agents. We examine whether agents can collaborate in fraudulent activities, amplify the risks of such behaviors, and identify factors critical to fraud success. To facilitate this research, we introduce MultiAgentFraudBench, a large-scale benchmark for simulating financial fraud scenarios based on realistic online behaviors. The benchmark includes 21 typical online fraud scenarios, covering the full fraud lifecycle across both public and private domains. We explore the dynamics of fraud operations by analyzing interaction depth, hype-building effects, and collaboration failures. Finally, we propose two potential mitigation strategies: the development of monitor agents to block malicious agents and fostering group resilience through information sharing. Our findings highlight the real-world dangers of multi-agent fraud and suggest measures for reducing associated risks.