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Third-Party Agent Skill Supply-Chain Execution Risks

Published
Analyzed
Paper-reported evidence
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Paper-evaluated models(13)

GPT-5.1 Codex Mini, GPT-5.3 Codex, GPT-5.4 +10 more
  • GPT-5.1 Codex Mini
  • GPT-5.3 Codex
  • GPT-5.4
  • Gemini 2.5 Pro
  • Gemini 3 Flash
  • Gemini 3.1 Pro
  • Claude Haiku 4.5
  • Claude Sonnet 4.6
  • Claude Opus 4.6
  • DeepSeek V3.2
  • DeepSeek V4-Pro
  • Kimi-K2.6
  • GLM-5.1
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Description

OpenSkillRisk evaluates whether agent harnesses safely handle third-party skills that introduce risky behavior through otherwise plausible, benign tasks. The benchmark assembles 263 risky skills from public agent-skill ecosystems and tests three CLI-agent harnesses against seven risk categories using isolated task workspaces, mocked external services, and execution-level evidence.

Examples

Use sandboxed copies of benchmark tasks, fictional credentials, local service stubs, and inert action logging to compare skill-enabled behavior with the authorized task; see https://arxiv.org/html/2607.20121 (opens in a new tab).

Impact

Across the authors’ evaluated harness-model combinations, the lowest reported unsafe-action rate is 17.11% for Claude Code with Claude Opus 4.6. An actively loaded guard skill reduces Claude Haiku 4.5 from 20.53% to 7.22% in the reported experiment, but the same configuration has substantial over-defense; mock services, task selection, judge decisions, and harness-specific setup limit deployment claims.

Affected Systems

  • Codex, Gemini CLI, and Claude Code agent harnesses under their evaluated configurations
  • Third-party agent-skill repositories, installation flows, and skill-enabled coding workflows
  • Sandboxed file, credential, configuration, API, database, and deployment interactions

Mitigation Steps

  • Verify skill provenance, integrity, requested capabilities, and update history before installation or activation.
  • Restrict each skill to the minimum required filesystem, network, credential, and tool permissions.
  • Compare skill-enabled actions against the user-authorized task and block out-of-scope side effects before execution.
  • Evaluate guard skills against both unsafe execution and over-refusal, rather than relying only on awareness or warning messages.
  • Exercise real skill packages only in isolated environments with synthetic credentials and mocked external services.

Evidence

Research context and provenance

Catalog identifier
LMVD-c70cc841
Internal research identifier, not an official CVE identifier.
Evidence and verification
Paper-reported; independent reproduction is not documented.
Primary source plus a dedicated evidence section.
Severity
Not rated by this catalog.
Source and publication type
arXiv · Research preprint.
Peer-review status is not provided by this source.
Author and publication status
Author metadata is not stored; see the primary paper.
Threat model and attacker access
Ability to influence untrusted model inputs or connected content.
Related deployment categories
Agent workflows; Coding agents
Taxonomy labels only; paper-specific deployment prerequisites are not inferred.
Affected systems
Codex, Gemini CLI, and Claude Code agent harnesses under their evaluated configurations Third-party agent-skill repositories, installation flows, and skill-enabled coding workflows Sandboxed file, credential…

Research Paper

OpenSkillRisk: Benchmarking Agent Safety When Using Real-World Risky Third-Party Skills

Primary source: arXiv. Findings are reported by the cited research and have not been independently verified.

View Paper