SkillGuard: A Permission-Centric Framework for Agent Skill Security

2026-06-01 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

SkillGuard is a permission-centric framework designed to enhance the security and privacy of LLM agent skill ecosystems. It addresses the gap between a skill's declared intent and its runtime behavior by treating skills as permission-bearing executable artifacts. SkillGuard implements a dual-plane governance model, regulating both context influence and action side effects through components like skill manifests, runtime access control, user-mediated authorization, deny-by-default enforcement, capability inference, and behavior monitoring. Evaluated on 315 real-world skills, its permission taxonomy covers 99.76% of protected objects, and automated manifest generation achieves 91.0% F1. In adversarial tests, SkillGuard reduced attack success rates from 32.37% to 23.02% for contextual injections and from 25.56% to 16.67% for obvious injections, while maintaining benign task utility with only a 1.45 percentage point decrease in Task Success Rate. Despite a median 20.9% increase in token consumption, it surprisingly reduced median wall-clock time by 20.9% by blocking malicious execution paths early.

Key takeaway

For AI Security Engineers developing or deploying LLM agents with third-party skills, you should integrate permission frameworks like SkillGuard to mitigate critical runtime risks. This approach ensures skills operate within declared boundaries, preventing unauthorized context injections or malicious tool usage. Implement manifest-driven policies and user-mediated authorization to enforce least privilege and maintain accountability, significantly reducing attack surface without substantial utility loss.

Key insights

SkillGuard secures LLM agent skills by treating them as permission-bearing artifacts, governing both context influence and action side effects through a dual-plane model.

Principles

• Least Privilege: Grant minimum necessary permissions.
• Complete Mediation: Check every sensitive action at runtime.
• Fail-Safe Defaults: Deny operations unless explicitly authorized.

Method

SkillGuard employs skill manifests, runtime access control, user interaction, deny-by-default policy enforcement, and behavior monitoring to regulate agent skill operations.

In practice

• Define skill capabilities via a JSON-based manifest DSL.
• Map host tool invocations to canonical capabilities for policy checks.
• Utilize agent lifecycle hooks for portable security enforcement.

Topics

• LLM Agent Security
• Agent Skills
• Permission Frameworks
• Access Control
• Runtime Enforcement
• Policy Governance

Code references

• Dianshu-Liao/SkilLGuard

Best for: AI Architect, CTO, VP of Engineering/Data, AI Security Engineer, AI Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.