[2511.06448] When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms

Computer Science > Multiagent Systems arXiv:2511.06448 (cs) [Submitted on 9 Nov 2025 (v1), last revised 6 Apr 2026 (this version, v2)] Title:When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms Authors:Qibing Ren, Zhijie Zheng, Jiaxuan Guo, Junchi Yan, Lizhuang Ma, Jing Shao View a PDF of the paper titled When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms, by Qibing Ren and 5 other authors View PDF HTML (experimental) Abstract:In this work, we study the risks of collective financial fraud in large-scale multi-agent systems powered by large language model (LLM) agents. We investigate whether agents can collaborate in fraudulent behaviors, how such collaboration amplifies risks, and what factors influence fraud success. To support this research, we present MultiAgentFraudBench, a large-scale benchmark for simulating financial fraud scenarios based on realistic online interactions. The benchmark covers 28 typical online fraud scenarios, spanning the full fraud lifecycle across both public and private domains. We further analyze key factors affecting fraud success, including interaction depth, activity level, and fine-grained collaboration failure modes. Finally, we propose a series of mitigation strategies, including adding content-level warnings to fraudulent posts and dialogues, using LLMs as monitors to block potentially malicious agents, and fostering group resilience through...