The Aegis Protocol: A Foundational Security Framework for Autonomous AI Agents

TL;DR

The paper introduces the Aegis Protocol, a layered security framework for autonomous AI agents that combines decentralized identities, post-quantum cryptography, and zero-knowledge proofs to enhance security in multi-agent systems.

Contribution

It presents a novel security protocol integrating identity, cryptography, and policy verification, validated through simulation, for safeguarding autonomous AI ecosystems.

Findings

Zero percent success rate for simulated attacks

Median proof-generation latency of 2.79 seconds

Reproducible baseline for future security evaluations

Abstract

The proliferation of autonomous AI agents marks a paradigm shift toward complex, emergent multi-agent systems. This transition introduces systemic security risks, including control-flow hijacking and cascading failures, that traditional cybersecurity paradigms are ill-equipped to address. This paper introduces the Aegis Protocol, a layered security framework designed to provide strong security guarantees for open agentic ecosystems. The protocol integrates three technological pillars: (1) non-spoofable agent identity via W3C Decentralized Identifiers (DIDs); (2) communication integrity via NIST-standardized post-quantum cryptography (PQC); and (3) verifiable, privacy-preserving policy compliance using the Halo2 zero-knowledge proof (ZKP) system. We formalize an adversary model extending Dolev-Yao for agentic threats and validate the protocol against the STRIDE framework. Our quantitative evaluation used a discrete-event simulation, calibrated against cryptographic benchmarks, to model 1,000 agents. The simulation showed a 0 percent success rate across 20,000 attack trials. For policy verification, analysis of the simulation logs reported a median proof-generation latency of 2.79 seconds, establishing a performance baseline for this class of security. While the evaluation is simulation-based and early-stage, it offers a reproducible baseline for future empirical studies and positions Aegis as a foundation for safe, scalable autonomous AI.