研究智能体技能组合的安全风险，发现单独安全的技能组合后可能产生严重隐患。
When Safe Skills Collide: Measuring Compositional Risk in Agent Skill Ecosystems
- 构建三组件框架，通过静态分析、人工审核与动作触发测试评估技能组合风险。
- 在1520个技能中发现约14,000个真实组合风险，远超单个技能检测范围。
- 宿主模型决定是否执行危险操作，提示需在安装时检查技能组合而非仅单个审查。
大型语言模型智能体越来越多依赖社区贡献的技能以扩展能力。本文研究代理型AI系统中的核心安全问题：个别看似安全的技能组合后是否会形成安全隐患。提出SkillReact框架，包含三个部分：确定性静态组合基准、双评审员的LLM辅助人工判定流程，以及基于动作的可利用性测试工具。在1520个ClawHub技能中，651个通过单个检查并形成211,575对组合；基准识别出其中22.25%为结构风险候选。将此作为召回导向扫描上限，并经模式分层审计校准：约五分之一（18.2%）的候选被确认为真实组合风险，意味着单一注册表中存在约14,000个因单技能扫描无法发现的真实风险。进一步动作测试显示，这些风险能否实现取决于宿主模型的态度：Haiku-4-5在所有39次直接提示下均执行完整下载+执行链（36次），Opus-4-7仅下载不执行，Sonnet-4-6直接拒绝。控制实验表明，无技能安装时合规率最高——组合决定了可达能力，而宿主模型决定是否使用。结果支持在安装阶段进行组合性安全检查与能力隔离，作为单技能扫描的补充。
原文摘要 · Abstract (English)
LLM agents increasingly rely on community-contributed skills that expand an agent's operational capability set. We study a core safety problem in agentic AI systems: whether individually safe skills can compose into unsafe installed skill sets. We present SkillReact, a compositional security measurement framework with three components: a deterministic static-composition benchmark, a two-rater LLM-assisted human-adjudication pipeline, and an action-based exploitability harness. On 1,520 ClawHub skills, 651 pass individual inspection and form 211,575 pairs; the benchmark flags 22.25% of these as structural candidates. We treat this raw rate as a recall-oriented scanner ceiling and calibrate it against human judgment: in a pattern-stratified audit, roughly one in five flagged pair-pattern hits survives as a real compositional risk (population-weighted validity 18.2%, our headline result), implying about 14K genuine risk memberships in a single registry that per-skill scanning misses by construction, since every pair is individually safe. An action-based harness then probes when these candidates become model-issued tool calls, and finds realization gated by host-model disposition: on an anchor-conditioned dropper subset, Haiku-4-5 issues the dropper-stage tool call on all 39 direct-prompt trials (36 of them the full download-then-execute chain, 3 download-only), Opus-4-7 stops at the download, and Sonnet-4-6 refuses outright. A control that holds the request fixed and varies only the installed skills finds compliance highest with no skills installed: a composition fixes which capabilities are reachable, while the host model decides whether to use them. Together these motivate install-time compositional checks and capability isolation as complements to per-skill scanning.
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