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Building A Secure Agentic AI Application Leveraging A2A Protocol

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arxiv 2504.16902 v2 pith:4PAKNEZD submitted 2025-04-23 cs.CR cs.AI

Idan Habler , Ken Huang , Vineeth Sai Narajala , Prashant Kulkarni This is my paper
classification cs.CRcs.AI
keywords secureprotocolagentagenticanalysisbuildingcomplexdesigned
verification ladder T0 review T1 audit T2 compute T3 formal
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As Agentic AI systems evolve from basic workflows to complex multi agent collaboration, robust protocols such as Google's Agent2Agent (A2A) become essential enablers. To foster secure adoption and ensure the reliability of these complex interactions, understanding the secure implementation of A2A is essential. This paper addresses this goal by providing a comprehensive security analysis centered on the A2A protocol. We examine its fundamental elements and operational dynamics, situating it within the framework of agent communication development. Utilizing the MAESTRO framework, specifically designed for AI risks, we apply proactive threat modeling to assess potential security issues in A2A deployments, focusing on aspects such as Agent Card management, task execution integrity, and authentication methodologies. Based on these insights, we recommend practical secure development methodologies and architectural best practices designed to build resilient and effective A2A systems. Our analysis also explores how the synergy between A2A and the Model Context Protocol (MCP) can further enhance secure interoperability. This paper equips developers and architects with the knowledge and practical guidance needed to confidently leverage the A2A protocol for building robust and secure next generation agentic applications.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms

    cs.CR 2025-07 conditional novelty 6.0 of 10

    Function Calling and MCP architectures show distinct vulnerability patterns, with chained attacks succeeding 91-96% of the time in both.

  2. AI Agent Communications in AI-Native 6G Network: Status, Challenges and Opportunities

    cs.NI 2026-07 accept novelty 4.0 of 10

    A gap analysis showing that today's AI-native 6G specifications do not yet provide the semantic slicing, cross-layer orchestration, decentralized trust, and protocol adaptation that large-scale AI-agent communication ...

  3. SDEC: Semantic Deep Embedded Clustering

    cs.CL 2025-08 unverdicted novelty 4.0 of 10

    SDEC is described as a hybrid autoencoder and transformer embedding method that reportedly sets text clustering benchmarks, but the submission's body is a different, unrelated paper.

  4. Agent Capability Negotiation and Binding Protocol (ACNBP)

    cs.AI 2025-06 reject novelty 4.0 of 10

    ACNBP is a proposed standard for secure agent capability negotiation with an extension mechanism, but it lacks formal verification, experiments, and independent evaluation.

  5. The Trust Fabric: Decentralized Interoperability and Economic Coordination for the Agentic Web

    cs.CR 2025-07 reject novelty 3.0 of 10

    The paper presents a five-layer decentralized framework (Nanda) for agent discovery, trust scoring, and micropayments, but supports its deployment claims only with self-referential descriptions.

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