mlsanityData to 7 Oct 2026

A2AS: Agentic AI Runtime Security and Self-Defense

arXiv 2510.13825 ↗First posted 8 Oct 2025cs.CR cs.AIcited by 5

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 8 of its 12 references that are in the map. First posted on arXiv 8 Oct 2025.

Builds on

8 of 12 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Design Patterns for Securing LLM Agents against Prompt Injections10 Jun
  2. 2025Defeating Prompt Injections by Design24 Mar

Other references · 6

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
  3. 2024SecAlign: Defending Against Prompt Injection with Preference Optimizationcited by 179
  4. 2025Control Illusion: The Failure of Instruction Hierarchies in Large Language Modelscited by 31
  5. 2025SoK: Evaluating Jailbreak Guardrails for Large Language Modelscited by 17
  6. 2025No Free Lunch with Guardrailscited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Building A Secure Agentic AI Application Leveraging A2A Protocol
  2. 2026A2ABreak: Systematic Security Analysis of the A2A Protocol ↗
  3. 2026AgentRFC: Security Design Principles and Conformance Testing for Agent Protocols
  4. 2025Securing Agentic AI Systems -- A Multilayer Security Framework
  5. 2026A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework
  6. 2026Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP
  7. 2026A Framework for Formalizing LLM Agent Security
  8. 2025Systems Security Foundations for Agentic Computing