A2AS: Agentic AI Runtime Security and Self-Defense
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
- 2025Design Patterns for Securing LLM Agents against Prompt Injections10 Jun
- 2025Defeating Prompt Injections by Design24 Mar
Other references · 6
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
- 2024SecAlign: Defending Against Prompt Injection with Preference Optimizationcited by 179
- 2025Control Illusion: The Failure of Instruction Hierarchies in Large Language Modelscited by 31
- 2025SoK: Evaluating Jailbreak Guardrails for Large Language Modelscited by 17
- 2025No Free Lunch with Guardrailscited by 10
Built on by
Citing papers per year: 2025: 1; 2026: 4.
5 papers.
- 2026Intelligent AI Delegationcited by 16
- 2025Security Analysis of Agentic AI Communication Protocols: A Comparative Evaluationcited by 6
- 2026SMCP: Secure Model Context Protocolcited by 5
- 2026Deontic Policies for Runtime Governance of Agentic AI Systemscited by 5
- 2026SkillGuard: A Permission-Centric Framework for Agent Skill Securitycited by 4
- 2026Deontic Policies for Runtime Governance of Agentic AI Systems17 Jun 2026
- 2026SkillGuard: A Permission-Centric Framework for Agent Skill Security2 Jun 2026
- 2026Intelligent AI Delegation12 Feb 2026
- 2026SMCP: Secure Model Context Protocol1 Feb 2026
- 2025Security Analysis of Agentic AI Communication Protocols: A Comparative Evaluation5 Nov 2025
Similar, not cited
Close in wording, no citation either way
- 2025Building A Secure Agentic AI Application Leveraging A2A Protocol
- 2026A2ABreak: Systematic Security Analysis of the A2A Protocol ↗
- 2026AgentRFC: Security Design Principles and Conformance Testing for Agent Protocols
- 2025Securing Agentic AI Systems -- A Multilayer Security Framework
- 2026A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework
- 2026Security Threat Modeling for Emerging AI-Agent Protocols: A Comparative Analysis of MCP, A2A, Agora, and ANP
- 2026A Framework for Formalizing LLM Agent Security
- 2025Systems Security Foundations for Agentic Computing