# VIGIL: Runtime Enforcement of Behavioral Specifications in AI Agent Skills

arXiv: https://arxiv.org/abs/2606.26524 · HTML: https://mlsanity.com/paper/2606.26524/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 28 of its 63 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 28 of 28)

- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks](https://mlsanity.com/paper/2602.12670/) (2026)
- [Defeating Prompt Injections by Design](https://mlsanity.com/paper/2503.18813/) (2025)
- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [SoK: Agentic Skills -- Beyond Tool Use in LLM Agents](https://mlsanity.com/paper/2602.20867/) (2026)
- [Securing AI Agents with Information-Flow Control](https://mlsanity.com/paper/2505.23643/) (2025)
- [SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents](https://mlsanity.com/paper/2412.13178/) (2024)
- [Agent Skills for Large Language Models: Architecture, Acquisition, Security, and the Path Forward](https://mlsanity.com/paper/2602.12430/) (2026)
- [Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale](https://mlsanity.com/paper/2601.10338/) (2026)
- [Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks](https://mlsanity.com/paper/2602.20156/) (2026)
- [Contextual Agent Security: A Policy for Every Purpose](https://mlsanity.com/paper/2501.17070/) (2025)
- [AgentArmor: Enforcing Program Analysis on Agent Runtime Trace to Defend Against Prompt Injection](https://mlsanity.com/paper/2508.01249/) (2025)
- [Cedar: A New Language for Expressive, Fast, Safe, and Analyzable Authorization (Extended Version)](https://mlsanity.com/paper/2403.04651/) (2024)
- [EchoLeak: The First Real-World Zero-Click Prompt Injection Exploit in a Production LLM System](https://mlsanity.com/paper/2509.10540/) (2025)
- [AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification](https://mlsanity.com/paper/2602.22724/) (2026)
- [The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey](https://mlsanity.com/paper/2603.11088/) (2026)
- [SkillProbe: Security Auditing for Emerging Agent Skill Marketplaces via Multi-Agent Collaboration](https://mlsanity.com/paper/2603.21019/) (2026)
- [Ghost in the Agent: Redefining Information Flow Tracking for LLM Agents](https://mlsanity.com/paper/2604.23374/) (2026)
- [AgentCgroup: Understanding and Controlling OS Resources of AI Agents](https://mlsanity.com/paper/2602.09345/) (2026)
- [Agent-Diff: Benchmarking LLM Agents on Enterprise API Tasks via Code Execution with State-Diff-Based Evaluation](https://mlsanity.com/paper/2602.11224/) (2026)
- [Towards Secure Agent Skills: Architecture, Threat Taxonomy, and Security Analysis](https://mlsanity.com/paper/2604.02837/) (2026)
- [AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skills](https://mlsanity.com/paper/2605.13940/) (2026)
- [HarmfulSkillBench: How Do Harmful Skills Weaponize Your Agents?](https://mlsanity.com/paper/2604.15415/) (2026)
- [Semia: Auditing Agent Skills via Constraint-Guided Representation Synthesis](https://mlsanity.com/paper/2605.00314/) (2026)
- [No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skills](https://mlsanity.com/paper/2605.13044/) (2026)
- [FORAY: Towards Effective Attack Synthesis against Deep Logical Vulnerabilities in DeFi Protocols](https://mlsanity.com/paper/2407.06348/) (2024)
- [Policy-Invisible Violations in LLM-Based Agents](https://mlsanity.com/paper/2604.12177/) (2026)

## Built on by (most cited first, 9 of 9)

- [FAVA: Formal Authorization for Verified Agents with Evidence-Backed Permission Graphs](https://mlsanity.com/paper/2607.27267/) (2026)
- [Forensic Trajectory Signatures for Agent Memory Poisoning Detection](https://mlsanity.com/paper/2606.30566/) (2026)
- [EvoUndo: Recoverability-Constrained Self-Evolution for LLM Agent Harnesses](https://mlsanity.com/paper/2608.28363/) (2026)
- [Versioned Transitive Dependency-Closure Binding and Operation-Time Effect Governance for Agent Skills: ClosureBound](https://mlsanity.com/paper/2609.05920/) (2026)
- [Testing and Evaluation of Agentic AI Systems In Military Command and Control](https://mlsanity.com/paper/2608.20597/) (2026)
- [LeaseGuard: Incumbent-Preserving Admission Control for Privileged LLM Agents](https://mlsanity.com/paper/2609.24077/) (2026)
- [Who Holds the Pen? Let Specifications, Not Agents, Sign Off](https://mlsanity.com/paper/2609.29921/) (2026)
- [Authorization Closure Graph: Minimal Repair for LLM Agents with Evolving User Instructions](https://mlsanity.com/paper/2609.32428/) (2026)
- [Runtime Authorization of Self-Generated Subgoals in Long-Horizon Tool-Using AI Agents](https://mlsanity.com/paper/2610.04975/) (2026)

## Abstract (arXiv)

Agentic systems increasingly act through third-party skills, allowing model-generated decisions to affect files, communication channels, and cyber-physical devices. These skills often include natural-language specifications that define access permissions, disclosure limits, execution privileges, and required preconditions. Although such specifications describe the intended boundaries of skill behavior, they do not by themselves provide executable runtime enforcement. Enforcing them raises a contextual granularity challenge: even when a policy is written for a particular task context, a monitor must still decide which events to observe, what state to retain, how far across the execution to reason, and where to intervene. Choosing the wrong granularity can either block benign executions or miss violations that emerge only across multiple actions. Most existing enforcement mechanisms, however, assume a fixed event model or enforcement point. In this work, we present VIGIL, an end-to-end runtime enforcement framework for agentic systems. VIGIL checks an agent's actual execution trace against behavioral policies from skill specifications, operator-defined constraints, and global rules spanning multiple skills. To make such policies executable, VIGIL introduces a policy language that captures context-specific enforcement requirements over agent-tool events, including temporal dependencies, argument constraints, and value-flow conditions. The language is paired with symbolic evaluation rules that translate policies into SMT constraints over finite traces, allowing VIGIL to detect violations that depend on event order, argument relationships, or cross-call value flow rather than relying on fixed single-call filters. On real LLM-agent runs spanning office-document, operational, and engineering tasks, VIGIL detects policy violations with over 95% recall and a false-positive rate below 10%.