Agentic AI Runtime Security and Self-Defense (2025)
The A2AS framework has been introduced as a security layer for AI agents and applications. It aims to enforce certified behavior and ensure context window integrity while avoiding operational complexities. This paper outlines the BASIC security model that serves as the foundation for the A2AS framework.
- ▪The A2AS framework is designed to secure AI agents and LLM-powered applications, similar to HTTPS for HTTP.
- ▪It defines security boundaries, authenticates prompts, and applies custom policies to control agentic behavior.
- ▪The BASIC security model includes behavior certificates, authenticated prompts, security boundaries, in-context defenses, and codified policies.
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Record
| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2510.13825 |
| Publication time | Mon, 18 May 2026 18:17:46 +0000 |
| Retrieval time | 2026-05-18T18:29:56.932Z |
| Last seen | 2026-05-18T18:29:56.932Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | fcRiQyLv_GqP |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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 View a PDF of the paper titled A2AS: Agentic AI Runtime Security and Self-Defense, by Eugene Neelou and 17 other authors View PDF 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv.org.
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