针对大模型技能复用，提出多轨迹溯源审计框架，精准识别跨场景技能复制。
SkillTrace: Multi-Trace Provenance Auditing for LLM-Agent Skill Reuse
- 从表达、实现、操作三方面提取技能复用痕迹，构建技能操作图（SOG）
- 在820个变换后的正例上达到AUROC 0.938、F1 0.898，显著优于传统方法
- 适用于平台级技能审核，可生成可操作的复用审查队列，适合安全与合规团队
LLM-agent生态系统中，可复用技能（混合模态的元数据、自然语言指令、代码、工具、参考文献和操作流程）正成为市场商品。现有检测方法仅关注单一模态代码或整包相似性，难以捕捉仅保留部分特征的技能复用。为此，我们提出SKILLTRACE，一种多轨迹溯源审计框架。该框架提取三种溯源痕迹：表达（Expression）、实现（Implementation）和操作（Operational）。其中，操作痕迹以技能操作图（Skill Operational Graph, SOG）形式表示，捕获激活、流程与资源流结构。仅在技能入库时由大模型辅助一次完成操作痕迹提取；审计阶段通过确定性比对缓存痕迹，并以同功能严格负样本校准，输出支持复用决策的痕迹。在包含820个转化后正例、100个市场锚点及751个负样本的SKILLTRACE-BENCH上，取得AUROC 0.938、F1 0.898。对36,446个真实技能的大规模审计进一步表明，基于痕迹归属的证据能揭示超越仓库级基线的可操作复用审查队列。
原文摘要 · Abstract (English)
LLM-agent ecosystems are rapidly growing around reusable skills: mixed-modality packages of metadata, natural-language instructions, code, tools, references, and operational workflows. As skills become marketplace artifacts, auditing their reuse is no longer the same problem as ordinary code clone detection. Existing detectors target single-modality source code or whole-package similarity, yet skill reuse evidence is distributed across authored text, implementation fragments, and operational structure. As a result, they can miss reuse that preserves only one part of a skill. We present SKILLTRACE, a multi-trace provenance auditing framework for LLM-agent skill reuse. SKILLTRACE extracts three provenance traces: Expression, Implementation, and Operational. It represents the Operational Trace as a Skill Operational Graph (SOG) that captures activation, procedure, and resource-flow structure. An LLM assists only the Operational-trace extraction, once at ingestion; at audit time SKILLTRACE compares cached traces deterministically, calibrates each trace against same-function strict negatives, and reports which trace supports a reuse decision. On SKILLTRACE-BENCH, with 820 transformed reuse positives over 100 marketplace anchors and 751 negative controls, SKILLTRACE achieves AUROC 0.938 and F1 0.898. A 36,446-skill wild audit further shows that trace-attributed evidence surfaces actionable reuse review queues beyond repository-level baselines.
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