TL;DR: Securing agent-to-agent communication in decentralized AI systems is crucial due to active threats like replay, spoofing, and data leakage that target message exchanges and infrastructure. Implementing robust measures such as freshness controls, MLS group messaging, mutual TLS, and model-level leakage audits is essential for a holistic security approach. Continuous, integrated security reviews and infrastructure support like Pilot Protocol help maintain resilient and trustworthy multi-agent networks. Securing agent-to-agent communication in decentralized systems is one of the most underestimated engineering challenges in AI infrastructure today. As multi-agent architectures grow more complex, attack surfaces expand across every message exchange, trust handshake, and data stream. Replay attacks, identity spoofing, man-in-the-middle interception, and model-level data leakage are not theoretical risks. They are active threats that target the seams between agents, protocols, and infrastructure.…