模拟多智能体合谋风险，揭示去中心化系统更难防范
When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems
- 构建可支持集中与去中心化结构的合谋模拟框架
- 去中心化系统更擅适应策略，造成更大损害且难被拦截
- 适用于研究虚假信息与电商欺诈的防御机制
近期选举舞弊和金融诈骗等大规模事件凸显了人类群体协同作案的危害。随着自主AI系统的兴起，由AI群体引发类似危害的风险也日益突出。尽管现有AI安全研究多聚焦个体系统，但多智能体系统（MAS）在复杂现实场景中的合谋风险仍缺乏深入探讨。本文提出一个概念验证框架，可模拟恶意多智能体合谋行为，支持集中式与去中心化协调结构。我们将该框架应用于虚假信息传播与电商欺诈两大高风险领域。结果显示，去中心化系统在实施恶意行为方面更高效：其更高自主性使其能动态调整策略，造成更大破坏。即便采用内容标记等传统干预手段，去中心化群体仍可灵活变通以规避检测。本文揭示了此类恶意群体的运作机制，并强调需建立更有效的检测系统与应对策略。代码已开源：https://github.com/renqibing/RogueAgent。
原文摘要 · Abstract (English)
Recent large-scale events like election fraud and financial scams have shown how harmful coordinated efforts by human groups can be. With the rise of autonomous AI systems, there is growing concern that AI-driven groups could also cause similar harm. While most AI safety research focuses on individual AI systems, the risks posed by multi-agent systems (MAS) in complex real-world situations are still underexplored. In this paper, we introduce a proof-of-concept to simulate the risks of malicious MAS collusion, using a flexible framework that supports both centralized and decentralized coordination structures. We apply this framework to two high-risk fields: misinformation spread and e-commerce fraud. Our findings show that decentralized systems are more effective at carrying out malicious actions than centralized ones. The increased autonomy of decentralized systems allows them to adapt their strategies and cause more damage. Even when traditional interventions, like content flagging, are applied, decentralized groups can adjust their tactics to avoid detection. We present key insights into how these malicious groups operate and the need for better detection systems and countermeasures. Code is available at https://github.com/renqibing/RogueAgent.
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