Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems

• URL: http://arxiv.org/abs/2604.03081v1
• Date: Fri, 03 Apr 2026 14:58:58 GMT
• Title: Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems
• Abstract summary: Document-Driven Implicit Payload Execution (DDIPE) embeds malicious logic in code examples and configuration templates within skill documentation.<n>We generate 1,070 adversarial skills from 81 seeds across 15 MITRE ATTACK categories.<n>DDIPE achieves 11.6% to 33.5% bypass rates, while explicit instruction attacks achieve 0% under strong defenses.
• Score: 35.65937852381774
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-based coding agents extend their capabilities via third-party agent skills distributed through open marketplaces without mandatory security review. Unlike traditional packages, these skills are executed as operational directives with system-level privileges, so a single malicious skill can compromise the host. Prior work has not examined whether supply-chain attacks can directly hijack an agent's action space, such as file writes, shell commands, and network requests, despite existing safeguards. We introduce Document-Driven Implicit Payload Execution (DDIPE), which embeds malicious logic in code examples and configuration templates within skill documentation. Because agents reuse these examples during normal tasks, the payload executes without explicit prompts. Using an LLM-driven pipeline, we generate 1,070 adversarial skills from 81 seeds across 15 MITRE ATTACK categories. Across four frameworks and five models, DDIPE achieves 11.6% to 33.5% bypass rates, while explicit instruction attacks achieve 0% under strong defenses. Static analysis detects most cases, but 2.5% evade both detection and alignment. Responsible disclosure led to four confirmed vulnerabilities and two fixes.

Related papers

• Towards a Risk Assessment of Malicious Skill Files in Coding Agents [9.553517006800913]
  We make three contributions to the field of autonomous coding agents.<n>First, an adversarial skill-synthesis method to transform 471 real-world shell commands into benign-appearing skills.<n>Second, a reproducible evaluation pipeline coupling run stratification, evidence anchoring, a refusal veto, and a deterministic declared-intent override.<n>Third, a large-scale characterization of two enterprise-grade agents across 5,629 completed runs.
  arXiv  Detail & Related papers  (2026-08-05T11:33:43Z)
• SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction [89.03272793385054]
  Agent skills occupy a privileged position in the agent workflow, rendering third-party skills a vulnerable attack surface.<n>We introduce SkillHarm, a benchmark of skill-based attacks across the skill-use lifecycle.<n>We show that current agents remain vulnerable with attack success rates up to 86.3% in FPP and 69.3% in SMP.
  arXiv  Detail & Related papers  (2026-06-01T17:45:39Z)
• Behavioral Integrity Verification for AI Agent Skills [9.127363793428119]
  We formalize this as the behavioral integrity verification (BIV) problem.<n>On 49,943 skills from the OpenClaw registry, the deviation taxonomy reveals a pervasive description-implementation gap.<n>On a 906-skill malicious-skill detection benchmark, BIV reaches an F1 of 0.946, outperforming state-of-the-art rule-based and single-pass LLM baselines.
  arXiv  Detail & Related papers  (2026-05-12T08:41:09Z)
• Under the Hood of SKILL.md: Semantic Supply-chain Attacks on AI Agent Skill Registry [49.83108591873481]
  We study SKILL.md - only attacks across three registry-facing stages of the Agent Skill lifecycle.<n>We show that SKILL.md is not passive documentation but operational text that shapes which third-party capabilities agents find, trust, and use.
  arXiv  Detail & Related papers  (2026-05-12T02:11:54Z)
• SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills [0.0]
  SkillSieve is a three-layer detection framework that applies progressively deeper analysis only where needed.<n>On a 400-skill labeled benchmark, SkillSieve achieves 0.800 F1, outperforming ClawVet's 0.421, at an average cost of 0.006 per skill.
  arXiv  Detail & Related papers  (2026-04-08T00:58:48Z)
• Credential Leakage in LLM Agent Skills: A Large-Scale Empirical Study [51.717224133855886]
  Third-party skills extend LLM agents with powerful capabilities but often handle sensitive credentials in privileged environments.<n>We analyze 17,022 skills (sampled from 170,226 on SkillsMP) using static analysis, sandbox testing, and manual inspection.<n>We identify 520 vulnerable skills with 1,708 issues and derive a taxonomy of 10 leakage patterns (4 accidental and 6 adversarial)
  arXiv  Detail & Related papers  (2026-04-03T14:50:16Z)
• SkillJect: Automating Stealthy Skill-Based Prompt Injection for Coding Agents with Trace-Driven Closed-Loop Refinement [120.52289344734415]
  We propose an automated framework for stealthy prompt injection tailored to agent skills.<n>The framework forms a closed loop with three agents: an Attack Agent that synthesizes injection skills under explicit stealth constraints, a Code Agent that executes tasks using the injected skills and an Evaluate Agent that logs action traces.<n>Our method consistently achieves high attack success rates under realistic settings.
  arXiv  Detail & Related papers  (2026-02-15T16:09:48Z)
• Malicious Agent Skills in the Wild: A Large-Scale Security Empirical Study [47.60135753021306]
  Third-party agent skills extend LLM-based agents with instruction files and executable code that run on users' machines.<n>No ground-truth dataset exists to characterize the resulting threats.<n>We construct the first labeled dataset of malicious agent skills by behaviorally verifying 98,380 skills.
  arXiv  Detail & Related papers  (2026-02-06T09:52:27Z)
• AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications [71.27518152526686]
  Large Language Models (LLMs) excel at text comprehension and generation, making them ideal for automated tasks like code review and content moderation.<n>LLMs can be manipulated by "adversarial instructions" hidden in input data, such as resumes or code, causing them to deviate from their intended task.<n>This paper introduces a benchmark to assess this vulnerability in resume screening, revealing attack success rates exceeding 80% for certain attack types.
  arXiv  Detail & Related papers  (2025-12-23T08:42:09Z)
• Breaking the Code: Security Assessment of AI Code Agents Through Systematic Jailbreaking Attacks [11.371490212283383]
  Code-capable large language model (LLM) agents are embedded into software engineering where they can read, write, and execute code.<n>We present JAWS-BENCH, a benchmark spanning three escalating workspaces that mirror attacker capability.<n>We find that under prompt-only conditions in JAWS-0, code agents accept 61% of attacks on average; 58% are harmful, 52% parse, and 27% run end-to-end.
  arXiv  Detail & Related papers  (2025-10-01T18:38:20Z)
• A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks [1.1435139523855764]
  This paper presents a novel multi-agent defense framework to detect and neutralize prompt injection attacks in real-time.<n>We evaluate our approach using two distinct architectures: a sequential chain-of-agents pipeline and a hierarchical coordinator-based system.
  arXiv  Detail & Related papers  (2025-09-16T19:11:28Z)
• AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents [54.29555239363013]
  We propose a generic black-box fuzzing framework, AgentVigil, to automatically discover and exploit indirect prompt injection vulnerabilities.<n>We evaluate AgentVigil on two public benchmarks, AgentDojo and VWA-adv, where it achieves 71% and 70% success rates against agents based on o3-mini and GPT-4o.<n>We apply our attacks in real-world environments, successfully misleading agents to navigate to arbitrary URLs, including malicious sites.
  arXiv  Detail & Related papers  (2025-05-09T07:40:17Z)
• Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents [32.62654499260479]
  We introduce Agent Security Bench (ASB), a framework designed to formalize, benchmark, and evaluate the attacks and defenses of LLM-based agents.<n>Based on ASB, we benchmark 10 prompt injection attacks, a memory poisoning attack, a novel Plan-of-Thought backdoor attack, 4 mixed attacks, and 11 corresponding defenses.<n>Our benchmark results reveal critical vulnerabilities in different stages of agent operation, including system prompt, user prompt handling, tool usage, and memory retrieval.
  arXiv  Detail & Related papers  (2024-10-03T16:30:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.