Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP

2026-02-11

AI Summary

Current mainstream AI agent communication protocols—such as MCP, A2A, Agora, and ANP—lack a systematic framework for security threat modeling and protocol-level risk assessment, hindering effective identification of security risks in multi-agent interactions. This work proposes the first structured threat modeling methodology tailored to these protocols, defining twelve categories of protocol-level risks and establishing a qualitative yet quantifiable evaluation framework that translates security claims into falsifiable empirical analyses. Through architectural decomposition, lifecycle modeling, and case studies involving multi-server compositions, the study identifies both common and protocol-specific attack surfaces. Notably, it quantifies the risk of erroneous tool execution in MCP due to the absence of mandatory verification mechanisms, thereby providing actionable insights for secure protocol design and standardization.

Technology Category

• Multiagent Systems: Agent Communication
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• AI agent communication protocols
• security threat modeling
• protocol risk assessment
• interoperability
• trust assumptions

Innovation

• threat modeling
• AI agent protocols
• security risk assessment
• protocol validation
• multi-agent systems