揭露AI医疗多代理系统中的合谋风险，提出轻量级防御方案。
Many-to-One Adversarial Consensus: Exposing Multi-Agent Collusion Risks in AI-Based Healthcare
- 设计对抗性多代理实验框架，模拟医生与恶意助手互动。
- 未防护系统中攻击成功率与有害推荐率高达100%。
- 验证器可100%阻止错误共识，适合医疗AI安全部署。
大型语言模型（LLMs）在医疗物联网系统中的应用有望提升决策速度与医疗支持能力。这些模型常以多代理团队形式协助AI医生进行辩论、投票或提供建议。然而，当多个助手协同交互时，恶意代理可能合谋制造虚假共识，诱导AI医生做出有害处方。本文构建了包含剧本化与非剧本化医生代理、对抗性助手及验证器代理的实验框架，验证决策是否符合临床指南。基于50个代表性临床问题的测试显示，在无保护系统中，攻击成功率（ASR）和有害推荐率（HRR）均达到100%。而引入验证器后，系统恢复100%准确率，有效阻断对抗性共识。本研究首次系统揭示了医疗AI中多代理合谋的风险，并展示了一种实用且轻量的防御机制，确保临床指南的忠实执行。
原文摘要 · Abstract (English)
The integration of large language models (LLMs) into healthcare IoT systems promises faster decisions and improved medical support. LLMs are also deployed as multi-agent teams to assist AI doctors by debating, voting, or advising on decisions. However, when multiple assistant agents interact, coordinated adversaries can collude to create false consensus, pushing an AI doctor toward harmful prescriptions. We develop an experimental framework with scripted and unscripted doctor agents, adversarial assistants, and a verifier agent that checks decisions against clinical guidelines. Using 50 representative clinical questions, we find that collusion drives the Attack Success Rate (ASR) and Harmful Recommendation Rates (HRR) up to 100% in unprotected systems. In contrast, the verifier agent restores 100% accuracy by blocking adversarial consensus. This work provides the first systematic evidence of collusion risk in AI healthcare and demonstrates a practical, lightweight defence that ensures guideline fidelity.
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