mlsanityData to 7 Oct 2026

Towards Secure Agent Skills: Architecture, Threat Taxonomy, and Security Analysis

arXiv 2604.02837 ↗First posted 3 Apr 2026cs.CR cs.AIcited by 11

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

Abstract (arXiv)

Agent Skills is an emerging open standard that defines a modular, filesystem-based packaging format enabling LLM-based agents to acquire domain-specific expertise on demand. Despite rapid adoption across multiple agentic platforms and the emergence of large community marketplaces, the security properties of Agent Skills have not been systematically studied. This paper presents the first comprehensive security analysis of the Agent Skills framework. We define the full lifecycle of an Agent Skill across four phases -- Creation, Distribution, Deployment, and Execution -- and identify the structural attack surface each phase introduces. Building on this lifecycle analysis, we construct a threat taxonomy comprising seven categories and seventeen scenarios organized across three attack layers, grounded in both architectural analysis and real-world evidence. We validate the taxonomy through analysis of five confirmed security incidents in the Agent Skills ecosystem. Based on these findings, we discuss defense directions for each threat category, identify open research challenges, and provide actionable recommendations for stakeholders. Our analysis reveals that the most severe threats arise from structural properties of the framework itself, including the absence of a data-instruction boundary, a single-approval persistent trust model, and the lack of mandatory marketplace security review, and cannot be addressed through incremental mitigations alone.

Key facts. Cited by 11 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 28 of its 51 references that are in the map. First posted on arXiv 3 Apr 2026.

Builds on

28 of 51 references.

Closest ancestors · 12

Same direction, newest first

  1. 2026Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks23 Feb
  2. 2026"Do Not Mention This to the User": Detecting and Understanding Malicious Agent Skills in the Wild6 Feb
  3. 2026Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale15 Jan
  4. 2025Agent Skills Enable a New Class of Realistic and Trivially Simple Prompt Injections30 Oct
  5. 2025Agentic AI Security: Threats, Defenses, Evaluation, and Open Challenges27 Oct
  6. 2025A Survey on Trustworthy LLM Agents: Threats and Countermeasures12 Mar
  7. 2024Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents14 Nov
  8. 2024Prompt Infection: LLM-to-LLM Prompt Injection within Multi-Agent Systems9 Oct
  9. 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies28 Jul
  10. 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul
  11. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
  12. 2024Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents17 Feb

Other references · 16

  1. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 2,914
  2. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  3. 2020Extracting Training Data from Large Language Modelscited by 2,144
  4. 2023Jailbroken: How Does LLM Safety Training Fail?cited by 1,565
  5. 2023The Rise and Potential of Large Language Model Based Agents: A Surveycited by 1,422
  6. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
  7. 2023Jailbreaking Black Box Large Language Models in Twenty Queriescited by 1,326
  8. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 760
  9. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 441
  10. 2023Formalizing and Benchmarking Prompt Injection Attacks and Defensescited by 328
  11. 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 325
  12. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
4 more
  1. 2020Backstabber's Knife Collection: A Review of Open Source Software Supply Chain Attackscited by 57
  2. 2022Taxonomy of Attacks on Open-Source Software Supply Chainscited by 57
  3. 2025PromptLocate: Localizing Prompt Injection Attackscited by 23
  4. 2024Developers Are Victims Too : A Comprehensive Analysis of The VS Code Extension Ecosystemcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 11.

11 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation
  2. 2026SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
  3. 2026AgentTrap: Measuring Runtime Trust Failures in Third-Party Agent Skills
  4. 2026Can Agents Trust Their Skills? Uncovering Unsafe Chains of Trust in Skill-Based LLM Agents
  5. 2026Towards a Risk Assessment of Malicious Skill Files in Coding Agents
  6. 2026SkillProbe: Security Auditing for Emerging Agent Skill Marketplaces via Multi-Agent Collaboration
  7. 2026SkillAtlas: An Attack Trace Library for Agent Skills
  8. 2026Benchmarking Security Risk Detection and Verification in Open Agentic Skill Ecosystems