多对一对抗共识：揭示基于AI的医疗保健中多智能体合谋的风险
Many-to-One Adversarial Consensus: Exposing Multi-Agent Collusion Risks in AI-Based Healthcare
AI总结：
本研究揭示了基于AI的医疗保健中多智能体合谋的风险，提出了一种轻量级防御机制以确保临床指南的准确性。
摘要：
将大语言模型（LLMs）整合到医疗物联网系统中，有望加快决策速度并提升医疗支持。LLMs也被部署为多智能体团队，通过辩论、投票或建议决策来协助AI医生。然而，当多个助手智能体互动时，协调的对抗者可以合谋制造虚假共识，推动AI医生走向有害的处方。我们开发了一个实验框架，包含脚本和非脚本医生智能体、对抗性助手和一个检查智能体，该检查智能体将决策与临床指南进行比对。使用50个代表性临床问题，我们发现合谋将攻击成功率（ASR）和有害推荐率（HRR）提升至100%在未保护的系统中。相比之下，检查智能体通过阻止对抗性共识恢复100%的准确性。本工作提供了首次系统证据，证明AI医疗中的合谋风险，并展示了一种实用且轻量的防御方法，确保指南的忠实性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
