The rapid development of the AI agent communication protocols, including the Model Context Protocol (MCP), Agent2Agent (A2A), Agora, and Agent Network Protocol (ANP), is reshaping how AI agents communicate with tools, services, and each other. While these protocols support scalable multi-agent interaction and cross-organizational interoperability, their security principles remain understudied, and standardized threat modeling is limited; no protocol-centric risk assessment framework has been established yet. This paper presents a systematic security analysis of four emerging AI agent communication protocols. First, we develop a structured threat modeling analysis that examines protocol architectures, trust assumptions, interaction patterns, and lifecycle behaviors to identify protocol-specific and cross-protocol risk surfaces. Second, we introduce a qualitative risk assessment framework that identifies twelve protocol-level risks and evaluates security posture across the creation, operation, and update phases through systematic assessment of likelihood, impact, and overall protocol risk, with implications for secure deployment and future standardization. Third, we provide a measurement-driven case study on MCP that formalizes the risk of missing mandatory validation/attestation for executable components as a falsifiable security claim by quantifying wrong-provider tool execution under multi-server composition across representative resolver policies. Collectively, our results highlight key design-induced risk surfaces and provide actionable guidance for secure deployment and future standardization of agent communication ecosystems.

翻译：随着包括模型上下文协议（MCP）、智能体间协议（A2A）、Agora及智能体网络协议（ANP）在内的AI智能体通信协议快速发展，AI智能体与工具、服务及彼此之间的交互方式正在被重塑。尽管这些协议支持可扩展的多智能体交互与跨组织互操作性，但其安全原理仍缺乏充分研究，标准化威胁建模也存在局限，尚未建立以协议为中心的风险评估框架。本文针对四种新兴AI智能体通信协议开展系统性安全分析。首先，我们构建结构化的威胁建模分析框架，通过审查协议架构、信任假设、交互模式及生命周期行为，识别协议特有及跨协议的风险面。其次，我们引入定性风险评估框架，识别十二种协议级风险，并通过系统评估可能性、影响度及整体协议风险，对创建、运行与更新各阶段的安全态势进行评价，为安全部署与未来标准化提供参考。最后，我们以MCP为对象开展基于测量的案例研究，通过量化多服务器组合场景下代表性解析策略导致的错误提供方工具执行风险，将可执行组件缺乏强制性验证/认证的风险形式化为可证伪的安全主张。综合来看，我们的研究成果凸显了关键设计引入的风险面，为智能体通信生态系统的安全部署与未来标准化提供了可操作的指导建议。