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

• URL: http://arxiv.org/abs/2508.19267v1
• Date: Fri, 22 Aug 2025 06:18:57 GMT
• Title: The Aegis Protocol: A Foundational Security Framework for Autonomous AI Agents
• Authors: Sai Teja Reddy Adapala, Yashwanth Reddy Alugubelly,
• Abstract summary: The proliferation of autonomous AI agents marks a paradigm shift toward complex, emergent multi-agent systems.<n>This paper introduces the Aegis Protocol, a layered security framework designed to provide strong security guarantees for open agentic ecosystems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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