How do I establish trust between agents in an agent-to-agent (A2A) system?

Establishing trust between agents in an Agent-to-Agent (A2A) system requires implementing zero-trust principles, mutual authentication, and capability-based security to manage authority and prevent unauthorized actions.

• Implement Zero Trust at Protocol Boundaries: Treat every cross-organizational interaction as untrusted and verify it. This means the MCP server should not trust the calling agent's identity claim unconditionally, and trust should be established through cryptographic attestation and bounded by policy, not assumed from network location or prior interaction. This addresses the OWASP LLM Top 10 risk of LLM07: Agent Impersonation.
• Utilize Mutual Authentication: Agents should mutually authenticate each other to prevent impersonation and collusion. The agent platform should consume identity from the enterprise IdP via OIDC/SAML with token exchange to derived agent credentials.
• Employ Capability-Based Security: Authority should be conveyed through unforgeable tokens that precisely define what an agent is permitted to do, including specific resources, actions, and time limits. This ensures that an agent cannot expand its authority beyond what is explicitly granted. This helps mitigate the A2A confused deputy threat.
• Attenuate Capability Tokens: At every handoff between agents, capability tokens should be attenuated, meaning an agent delegates a strictly narrower capability to another agent, never a broader one. This prevents authority from expanding within the system.
• Bind Intent to Action: Ensure that every consequential action references the originally attested intent, and authorization is re-derived from that intent. This prevents prompt injection from altering the authorization layer's understanding of the agent's intent. This aligns with the NIST AI RMF function of Govern by ensuring accountability and appropriate decision-making.
• Monitor for Unusual Patterns: Continuously monitor for unusual agent-to-agent patterns to detect potential collusion or unauthorized activities. This falls under the NIST AI RMF function of Evaluation & Observability.