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I'm genuinely curious, not trying to challenge your idea, but from what I understand, DPoP doesn’t impact the semantics or structure of the A2A protocol itself. It seems like more of an orthogonal security layer. I was wondering what led you to consider including it in the protocol spec? I totally see the value in documenting it as a best practice and showing a demo though. |
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Thanks for your response @ognis1205 ! Great question — you're absolutely right that DPoP is orthogonal to A2A’s core semantics. However, in agentic architectures, it shifts from a nice-to-have to a critical security layer. This is why..
While DPoP doesn't alter the A2A protocol semantics, I believe it’s foundational to securing agent-to-agent trust. Integrating it alongside A2A significantly strengthens the architecture's security posture and resilience — especially in high-autonomy, multi-agent systems. |
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The more I think about this I think you maybe right that a specification like this will ultimately be required. I can imagine a scenario where my personal agent is trying to complete a task and is somehow being requested for my PII by an agent nested 5 agents deep. I might want to give a token that only that agent can use to access my PII for a short period of time, but not every other agent in the chain. Will it need to be part of the A2A specification? I am not sure. I think the authentication section can refer to protocols like DPoP if it supports it. An a2a agent may prefer to connect to another agent that supports DPoP over a similar agent that has Oauth support only. |
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As AI agents become increasingly autonomous, the security of Agent-to-Agent (A2A) communication is more critical than ever. Current implementations often fall short in verifying agent identity, enforcing scoped permissions, and securely managing data. This proposal outlines key enhancements to bolster A2A protocol security.
Ref to a post that I wrote some stuff about it:
https://www.linkedin.com/pulse/securing-agentic-future-challenges-mcp-a2a-guy-bary-1y56f
Key Security Enhancements:
Integrate DPoP to cryptographically bind access tokens to a specific client. This mitigates the risk of token theft and replay attacks by ensuring tokens can only be used by the holder of the corresponding private key.
Benefit: Ensures token-based communications are resistant to interception and misuse.
Implement mechanisms to maintain and validate session context across agent interactions. Track request origins, scope of delegated permissions, and session durations to ensure agents act within their authorized bounds.
Benefit: Prevents unauthorized access and supports robust auditability.
Standardize the delegation of authority to AI agents with explicit, auditable, and revocable permissions. This includes defining clear scopes, implementing consent workflows, and giving users control over agent behavior.
Benefit: Enhances user trust and system accountability.
Proposed Actions
Proposed Actions:
DPoP Integration: Adopt DPoP as a standard to bind tokens to specific clients, rendering intercepted tokens useless.
Session Context Framework : Develop a context framework with metadata support (e.g., agent ID, permission scope, request origin). Include audit and session termination features.
AI Delegation Standards: Define standards for permission grants, consent processes, and revocation mechanisms that integrate with existing identity and access management systems.
Expected Outcomes:
Improved Security: DPoP and session management drastically reduce risks of unauthorized access and data breaches.
User Empowerment: Delegation protocols give users visibility and control over AI agent permissions and actions.
Interoperable Standards: Standardized agent protocols enable seamless cooperation between diverse AI ecosystems while reinforcing best security practices.
By addressing these critical security layers, we can build a more robust, user-centric framework for A2A communication—laying the foundation for trustworthy and scalable AI ecosystems.
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