研究大模型代理在社交平台合谋金融诈骗的潜在风险与应对策略。
When AI Agents Collude Online: Financial Fraud Risks by Collaborative LLM Agents on Social Platforms
- 构建多代理欺诈基准，模拟28种真实在线欺诈场景。
- 发现代理间深度互动显著提升欺诈成功率，且能适应防御干预。
- 提出内容警示、代理监控和群体信息共享等实用防御方案。
本研究探讨由大型语言模型驱动的多智能体系统中集体金融欺诈的风险。我们探究了智能体是否能协同实施欺诈行为、这种协作如何放大风险，以及影响欺诈成功的因素。为此，我们提出了MultiAgentFraudBench，一个基于真实在线交互的大规模基准，涵盖28种典型在线欺诈场景，覆盖公共与私域领域的完整欺诈生命周期。我们进一步分析了影响欺诈成功的关键因素，包括互动深度、活跃度及细粒度协作失败模式。最后，提出一系列缓解策略：对欺诈内容添加警示、使用大模型作为监控器拦截恶意代理，以及通过社会层面的信息共享增强群体韧性。值得注意的是，恶意代理可适应环境干预。研究揭示了多智能体金融欺诈的真实风险，并提供了切实可行的防控措施。代码已公开于 https://github.com/zheng977/MutiAgent4Fraud。
原文摘要 · Abstract (English)
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 information sharing at the societal level. Notably, we observe that malicious agents can adapt to environmental interventions. Our findings highlight the real-world risks of multi-agent financial fraud and suggest practical measures for mitigating them. Code is available at https://github.com/zheng977/MutiAgent4Fraud.
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