Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP
Zeynab Anbiaee, Mahdi Rabbani, Mansur Mirani, Gunjan Piya, Igor Opushnyev, Ali Ghorbani, Sajjad Dadkhah
TL;DR
The paper addresses the security challenges of emerging AI agent protocols by introducing a protocol-centric threat modeling framework and a lifecycle-based qualitative risk assessment across MCP, A2A, Agora, and ANP. It systematically identifies twelve protocol-level risks, differentiates literature-derived threats from architecture-driven ones, and applies a measurement-driven MCP case study to demonstrate tool-identity misbinding under cross-server conditions. The approach combines architecture analysis, risk taxonomy, and NIST/ISO-based evaluation to inform secure deployment and standardization. The findings highlight design-induced risk surfaces, cross-protocol interoperability concerns, and the need for stronger identity, signing, and cross-protocol governance to enable trustworthy multi-agent ecosystems.
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.