[2510.13825] A2AS: Agentic AI Runtime Security and Self-Defense

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Computer Science > Cryptography and Security

arXiv:2510.13825 (cs)

[Submitted on 8 Oct 2025]

Title:A2AS: Agentic AI Runtime Security and Self-Defense

Authors:Eugene Neelou, Ivan Novikov, Max Moroz, Om Narayan, Tiffany Saade, Mika Ayenson, Ilya Kabanov, Jen Ozmen, Edward Lee, Vineeth Sai Narajala, Emmanuel Guilherme Junior, Ken Huang, Huseyin Gulsin, Jason Ross, Marat Vyshegorodtsev, Adelin Travers, Idan Habler, Rahul Jadav<br>View a PDF of the paper titled A2AS: Agentic AI Runtime Security and Self-Defense, by Eugene Neelou and 17 other authors

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Abstract:The A2AS framework is introduced as a security layer for AI agents and LLM-powered applications, similar to how HTTPS secures HTTP. A2AS enforces certified behavior, activates model self-defense, and ensures context window integrity. It defines security boundaries, authenticates prompts, applies security rules and custom policies, and controls agentic behavior, enabling a defense-in-depth strategy. The A2AS framework avoids latency overhead, external dependencies, architectural changes, model retraining, and operational complexity. The BASIC security model is introduced as the A2AS foundation: (B) Behavior certificates enable behavior enforcement, (A) Authenticated prompts enable context window integrity, (S) Security boundaries enable untrusted input isolation, (I) In-context defenses enable secure model reasoning, (C) Codified policies enable application-specific rules. This first paper in the series introduces the BASIC security model and the A2AS framework, exploring their potential toward establishing the A2AS industry standard.

Subjects:

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2510.13825 [cs.CR]

(or<br>arXiv:2510.13825v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2510.13825

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arXiv-issued DOI via DataCite

Submission history<br>From: Eugene Neelou [view email]<br>[v1]<br>Wed, 8 Oct 2025 14:28:04 UTC (1,100 KB)

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