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

arXiv:2602.11327 · cs.CR, cs.AI · Submitted 2026-02-11 · Read on arXiv

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Introduction to the show: ident: Security Radio. Generated commentary on the latest security and cryptography papers.

Nadia: I'm Nadia, and with me are Elias and Priya, guest researcher.

Elias: Today's paper: "Security Threat Modeling for Emerging AI-Agent Protocols".

Nadia: This paper presents a systematic security analysis of four emerging AI agent communication protocols—Model Context Protocol (MCP), Agent2Agent (A2A), Agora,

Elias: First, who's behind it and why it matters.

Title and authors: Nadia: So, the summary of "Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP" essentially outlines that the rapid development of communication protocols for AI agents is outpacing our ability to establish standardized threat modeling. They argue that examining isolated weaknesses in each protocol isn't enough because system-level risks emerge from how these different architectures interact.

Elias: They are emphasizing the need for a protocol-centric perspective, integrating threat modeling, architectural analysis, and lifecycle assessment across all four protocols to get a unified view of the vulnerability classes they’re seeing. It's about seeing the ecosystem risk rather than just protocol risk.

Priya: I think it’s significant that they explicitly state their selection criteria for these four protocols were popularity and maturity, which tells us a lot about where the research community is currently focusing its attention when looking at agent communication. That suggests these are the most active areas right now.

Nadia: Precisely, Priya. They then detail specific threats categorized into three impact domains: security threats addressing authentication and access control, supply chain and ecosystem integrity risks, and operational integrity and reliability concerns. It’s a very structured way to look at potential failures.

Elias: I find the breakdown into those specific domains helpful because it allows us to categorize the underlying cryptographic or architectural flaws more clearly than just saying "it's insecure." For instance, they pinpoint things like installer spoofing under supply chain integrity.

Priya: Those operational threats are what concern me most in terms of real-world deployment; if an agent can escape its sandbox or shadow a workflow at runtime, that directly impacts the reliability of whatever task it’s supposed to be performing. That's where privacy and data handling get messy.

Nadia: And they also cover update and maintenance risks, like post-update privilege persistence. It shows the paper is looking at security throughout the entire life of a protocol implementation, not just when it's first designed.

Elias: That lifecycle view is what elevates this analysis; it connects the initial design choices to potential failures during long-term operation and maintenance cycles. It makes the risk assessment much more robust than a snapshot analysis.

Priya: So, to put it simply, the paper is creating a comprehensive checklist for security engineers that covers how these AI agents communicate, from when they are first conceived to when they are being updated in production environments.

Nadia: Exactly. It moves the conversation from "is this protocol safe?" to "how does this protocol behave securely across its entire lifespan?" This sets the stage perfectly for what they suggest as improvements next.

The paper's summary: Elias: When we look at the suggested improvements in "Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP," it seems their main focus is on making those theoretical risk assessments actionable for developers. They are pushing for concrete technical requirements rather than just qualitative warnings.

Nadia: I agree. The paper suggests several specific technical fixes, like implementing a formally defined security extension to MCP that includes cryptographic identity anchoring and ephemeral access credentials for enterprise settings. That’s moving from abstract concepts to actual code requirements, which is what we need when discussing exploitation costs.

Priya: I'm interested in the part about defining a minimal canonical mapping—identity plus capability plus provenance—and explicitly binding it to the protocol context. That sounds like a way to enforce strict control over what an agent is allowed to do based on who it is and where it’s operating.

Elias: That canonical mapping idea directly tackles the inter-protocol risk we talked about earlier, trying to define a baseline contract for identity validation regardless of which specific protocol—MCP or ANP—is being used underneath. It tries to prevent relay and downgrade attacks when systems talk to each other.

Nadia: And they are pushing for automated update integrity verification across all protocols, requiring cryptographic signatures for any new component or protocol document modification before deployment. That’s a necessary step against supply chain poisoning that we discussed earlier, making the maintenance phase much safer.

Priya: If they can enforce verifiable permission scoping in MCP, it means that even if an agent is authenticated, its actions are strictly limited to what was explicitly granted at that moment. That directly addresses the concern about over-privileged agents causing unintended consequences.

Elias: Those improvements are very focused on the binding mechanism; they want to ensure that the identity isn't just a label but is cryptographically tied into every executable component or credential used during operation. It’s about making sure that when an agent runs something, we know exactly who is running it and what authority they have.

Nadia: So, the gist of the improvements is moving from identifying risks to prescribing specific cryptographic and structural controls across the lifecycle of these protocols. It's a blueprint for secure development in this emerging field.

The paper's improvements: Nadia: Wrapping up our discussion on "Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP," the paper concludes that no single protocol offers complete protection across its entire lifecycle. They found that while each has strengths—like ANP’s strong W3C DID and E2E encryption during creation—none cover all the bases.

Elias: I agree with that assessment; the analysis clearly shows that cross-protocol security standards are needed to bridge those gaps arising from different trust assumptions when these protocols are combined. The paper effectively proves that combining them introduces new, complex vulnerabilities.

Priya: From a privacy standpoint, this reinforces the idea that we need layered defenses because relying on one protocol's security isn't enough; the risk multiplies when you link different communication methods together. It highlights why a unified approach to risk assessment is so vital for protecting user data in multi-agent systems.

Nadia: I think the real implication here is that designers and implementers can no longer treat these protocols as isolated pieces; they have to consider the entire ecosystem they are building into their security model from the very beginning. It forces a much more deliberate design process.

Elias: It’s a call for rigor in how we define identity and authorization binding assumptions early on, because those assumptions dictate what kind of failure surface we end up with down the line when things go wrong. That foundational work is what matters most to me as a cryptographer.

Priya: I just hope the authors follow through on those recommendations for cross-protocol standards, because without that standardization, we're left chasing individual fixes instead of building a resilient infrastructure for AI interactions.

Nadia: Absolutely, they’ve laid out the groundwork for what needs to be done next in making these AI agent ecosystems more secure and trustworthy. That’s our analysis on this paper for now.

Conclusion: Nadia: So, we've spent our time walking through this paper on "Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP," and it really shows how the security landscape for these things is messy right now.

Elias: Indeed. The core finding is that we can't just treat each protocol in isolation; the structural weaknesses they share regarding authentication and integrity are what truly matter when you look at the whole system.

Priya: And from my side, the measurement-driven case study on MCP was really telling because it showed a design ambiguity translating directly into a reproducible security failure when identity wasn't bound to executable components. That’s something we need to track closely for privacy risks during operation.

Nadia: Exactly, Priya, and that ties back into the operational integrity threats they identified, like sandbox escapes; if you can't trust the runtime environment, all our agent work is compromised.

Elias: And I want to stress that this framework forces us to look at the lifecycle—creation through maintenance—because a vulnerability in the update phase is just as dangerous as one in the initial setup.

Priya: That lifecycle view makes it clear that we can't just secure the initial handshake; we have to secure every single interaction throughout its entire existence.

Nadia: So, what does this mean for us when building new AI systems? It means we need to mandate cryptographic identity anchoring and strict scoping from day one instead of trying to patch it later.

Elias: Precisely. The future work they suggest—defining a minimal canonical mapping for identity and capability—is the actual blueprint we should be aiming for in our next specifications.

Priya: I just hope the community takes this as seriously as we do, because securing these communication channels is fundamental to any serious privacy research or application of AI.

Nadia: Well, that's a wrap on this deep dive into the security threats across MCP, A2A, Agora, and ANP; we’ll be back next time when we look at how these agents interact with energy data protocols.

Canadian Institute for Cybersecurity (CIC) · Mastercard Vancouver Tech Hub

cs.CR, cs.AI

Submitted: 2026-02-11

Updated: 2026-04-17

Journal ref: Journal of Information Security and Applications, 2026, Article 104645

DOI: 10.1016/j.jisa.2026.104645

Code: https://github.com/a2aproject/A2A

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 92/100

The gist: This paper presents a systematic security analysis of four emerging AI agent communication protocols—Model Context Protocol (MCP), Agent2Agent (A2A), Agora, and Agent Network Protocol (ANP)—to

Key concepts

Threat Modeling Analysis
A structured method used to examine protocol architectures, trust assumptions, interaction patterns, and lifecycle behaviors. This process identifies specific security risks unique to each protocol and where different protocols interact with one another.
Lifecycle-Based Risk Assessment Framework
A five-step methodology based on NIST SP 800-30 used to assess risk across the entire development of a protocol—from creation to maintenance. It measures threat sources, vulnerability likelihood, impact magnitude, and calculates final risk using the formula R = L × I.
Identity/Authorization Binding Assumptions
Assumptions made about how identity and permissions are linked within a protocol. Weak assumptions here create 'trust boundaries' that attackers can exploit. The study focuses on how well protocols ensure that an agent's identity is cryptographically tied to the tools or actions it performs.
Cross-Protocol Security Standards
The urgent need for unified rules that apply across different AI communication protocols. These standards must define a 'minimal canonical mapping' of identity and capability to prevent attacks like relay or downgrade attacks when agents use multiple protocols together.

Terminology

Summary

This paper presents a systematic security analysis of four emerging AI agent communication protocols—Model Context Protocol (MCP), Agent2Agent (A2A), Agora, and Agent Network Protocol (ANP)—to establish a protocol-centric risk assessment framework for secure deployment.

The gist

This paper presents the first systematic and focused review of the security of emerging AI agent communication protocols at a time when the pace of industry adoption is much faster than the security maturity of the ecosystem, showing that these protocols share common structural weaknesses in authentication, supply chain integrity, operational reliability, and so on.

Structured Threat Modeling Analysis

The research develops 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. This analysis is used to derive a catalog of design-induced threat hypotheses for MCP, A2A, ANP, and Agora, grounded in factors such as trust boundaries and identity/authorization binding assumptions. The study also provides a qualitative risk assessment framework that identifies twelve protocol-level risks across the creation, operation, and update phases.

Protocol-Specific Risk Identification

The paper details specific threats categorized into three impact domains: "security threats address authentication & access control," "supply chain & ecosystem integrity, and operational integrity & reliability." For instance, under Authentication & Access Control, identified risks include Lack of authentication (noted in early MCP versions), and for Supply Chain Integrity, the taxonomy includes Installer Spoofing and Tool Poisoning. Operational Integrity focuses on threats like Sandbox Escape and Runtime Workflow Shadowing, while Update & Maintenance risks involve issues such as Post-update Privilege Persistence.

Measurement-Driven Case Study on MCP

The authors provide a measurement-driven case study on MCP that formalizes the risk of missing mandatory identity binding validation for executable components as a falsifiable security claim. This involves quantifying wrong-provider tool execution under multi-server composition across representative resolver policies. The experiment demonstrates that when identical tools are present but the identity is not cryptographically bound to the provider, a non-zero VR can be observed, showing how a design-level ambiguity can translate into a concrete, reproducible security failure.

Lifecycle-Based Risk Assessment Framework

The evaluation methodology follows NIST SP 800-30, involving five tasks: (1) identifying threat sources, (2) identifying vulnerabilities across the creation/configuration, operation, and update/maintenance stages; (3) determining the likelihood of occurrence based on Intrinsic exploitability and Environmental exposure; (4) determining the magnitude of impact using a three-level scale for CIA Impact and Operational / System Consequences; and finally (5) calculating risk using the formula R = L × I. This framework maps protocol activities to these lifecycle stages to compare how protocols manage identity validation, component registration, integrity verification, and namespace governance.

Comparative Risk Findings Across Protocols

The analysis shows that while each protocol has strengths, none of them offers complete protection across the entire lifecycle. For example, in the Creation/Configuration stage (Table 5), ANP is characterized by Strong W3C DID and E2E encryption, whereas MCP exhibits risks due to Weak or absent identity verification mechanisms. In the Operation stage (Table 6), MCP and Agora have a high overall risk due to factors like the lack of runtime code-integrity enforcement, while A2A presents a moderate risk because it lacks guarantees for semantic validation or strict token lifetime management. The analysis concludes that cross-protocol security standards are urgently required to mitigate risks arising from the combination of different trust assumptions.

Future Research Directions

The paper identifies key gaps, including the need to implement a formally defined security extension to MCP that includes cryptographic identity anchoring, ephemeral access credentials, and verifiable permission scoping for enterprise settings. Furthermore, it stresses that cross-protocol security standards must define a minimal canonical mapping (identity + capability + provenance) and include explicit binding to protocol context to mitigate relay and downgrade attacks when protocols are combined.

References

[1] H. Xiong, Z. Wang, X. Li, J. Bian, Z. Xie, S. Mumtaz, A. Al-Dulaimi, L. E. Barnes (2024).

[2] G. De Gasperis, S. D Facchini (2025).

[3] H Naveed et al., ACM Transactions on Intelligent Systems and Technology 16 (5) (2025).

[4] M Haenlein, A Kaplan (2019).

[5] J Luo et al., Large language model agent: A survey on methodology, applications and challenges, arXiv preprint arXiv:2503.21460 (2025).

[6] J S Park et al.

Improvements for AI systems

As a fastidious and diligent researcher, I have analyzed the provided scientific paper, Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP.

The paper identifies systemic security weaknesses across the lifecycle (Creation/Configuration, Operation, Update/Maintenance) of emerging AI agent communication protocols (MCP, A2A, Agora, ANP). The core findings point to failures in mandatory identity binding validation and lack of cryptographic provenance for executable components.

Here are the specific improvements that can be made to AI systems based on this research:

)

  1. Implement Mandatory Cryptographic Identity Binding for Executable Components (Addressing MCP Risk):

If an AI agent or tool is invoked via a protocol like MCP, the system must enforce a requirement where the tool's identity (its provider/source) is cryptographically bound to its execution signature. This means every executable component must carry a verifiable digital signature from its designated provider.

  1. Enhance Tool Selection and Invocation Logic (Addressing MCP Ambiguity):

Modify the client-side selection policy for tools within an MCP ecosystem to move beyond simple name/description matching. The system should be engineered to prioritize invocations based on cryptographic proof of origin (i.e., checking the provider's signature against a known whitelist or trust anchor) over heuristic scoring or ordering, thereby eliminating wrong-provider tool execution.

  1. Enforce Token Expiration and Strict Scoping for Agent Credentials (Addressing A2A Risk):

For protocols like A2A that rely on OAuth 2.0/JWT, the system must implement mandatory, non-extendable expiration durations for access tokens related to sensitive operations. Furthermore, token scopes must be refined to adhere strictly to the principle of least privilege; tokens should only grant the specific permissions necessary for a single task execution and should not allow for broad administrative or cross-domain access.

  1. Establish Automated Update Integrity Verification (Addressing All Protocols):

Integrate a mandatory mechanism into the protocol's update/maintenance phase that requires cryptographic verification (e.g., digital signatures) for all new software components, agent card updates, and protocol document (PD) modifications before they can be deployed or accepted by the running agents. This prevents poisoned updates and configuration drift.

  1. Implement Decentralized Identity Anchoring (Addressing ANP/General Trust):

For multi-agent environments utilizing protocols like ANP, mandate the use of decentralized identifiers (DIDs) for all agent identities. This ensures that identity is not reliant on a single central registry or self-declaration, providing a stronger foundation for trustless, end-to-end encrypted communication across heterogeneous networks.

  1. Develop Cross-Protocol Security Hardening Layers (Addressing Interoperability Risk):

To mitigate risks arising from the combination of different protocols (MCP + A2A + ANP), develop an interoperability layer that enforces a minimal canonical mapping for identity, capability, and provenance. This layer should explicitly define how agents must validate the binding between their protocol-specific context and the underlying resource provider's identity to prevent relay or downgrade attacks across ecosystems.

)

The improved AI system can achieve the following:

  1. Eliminate Wrong-Provider Tool Execution: The system will guarantee that when an agent requests a specific tool (e.g., Authorize Payment), it will execute that tool exclusively from the verified, legitimate provider, even if an attacker has deployed a server with an identical name or description.

  2. Enhance Confidentiality and Integrity: By enforcing strong token scoping and mandatory update signing, the system will prevent unauthorized data exfiltration (context leakage) during operation and stop malicious code/backdoors from being silently injected into the agent's runtime environment during maintenance.

  3. Ensure Robust Trust in Dynamic Networks: The adoption of DID-based identity anchoring will allow agents to establish trust relationships across different organizational boundaries securely, reducing reliance on potentially insecure centralized discovery mechanisms and preventing impersonation attacks during the creation phase.

  4. Provide Future-Proof Security: By formalizing security extensions for MCP that include verifiable permission scoping, the AI system will be suitable for use in high-stakes environments like financial transactions or safety-critical operations where a single misbinding event could cause catastrophic failure.

Sources

Related papers