Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains
Score
59Why selected
The paper introduces novel and practical ASSC concept and SkillDepAnalyzer tool, validated with large-scale data, revealing real security risks and offering actionable recommendations, with significant value for AI infrastructure and agent security.
AI commentary
「This research is the first to systematically uncover the dependency relationships and security risks among LLM agent skill packages, offering critical tools and design recommendations for building secure and maintainable intelligent agent ecosystems.」
Quick overview
Large Language Model (LLM) agent skills, while packaging reusable operational knowledge, suffer from opaque identity, version, and provenance information as they proliferate, causing duplicate dependencies and inconsistent installations. This paper introduces Agent Skill Supply Chains (ASSCs) to characterize mixed skill-package-service dependency graphs, and designs SkillDepAnalyzer—inspired by Software Bill of Materials (SBOMs)—to capture natural-language dependency evidence model skills as dependency-bearing artifacts. On the SKILL-DEP benchmark, SkillDepAnalyzer accurately recovers skill metadata and dependency graphs, outperforming LLM-based baselines and package-centric SBOM tools. Applied to over 1.43 million skills, the analysis reveals four structural patterns, hidden security signals in dependencies, and identifies known malicious skills persisting in ASSCs. Findings recommend typed dependency manifests, first-class dependency-cluster management, and lockfile-like records for skill developers.
- Introduces Agent Skill Supply Chains (ASSCs) to formalize mixed skill-package-service dependency graphs.
- Designs SkillDepAnalyzer to extract dependency evidence from natural language, accurately recovering metadata and dependency graphs.
- Analyzes over 1.43 million skills, discovering four structural patterns and hidden security risks in dependencies.
- Recommends typed dependency manifests, dependency-cluster management, and lockfile-like records for enhanced supply chain security.
Full text
Computer Science > Software Engineering
arXiv:2607.01136v1 (cs) [Submitted on 1 Jul 2026]Title:Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains
Authors:Changguo Jia, Tianqi Zhao, Runzhi He, Minghui Zhou View a PDF of the paper titled Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains, by Changguo Jia and 3 other authors View PDF HTML (experimental)Abstract:Agent skills package reusable operational knowledge for Large Language Model (LLM) agents, yet as they grow in scope, they become dependency-bearing artifacts whose identities, versions, and provenance remain implicit. This opacity already causes duplicated dependencies and inconsistent installations, exposing a gap that dependency management has yet to close. We introduce Agent Skill Supply Chains (ASSCs) to characterize mixed skill-package-service dependency graphs and help close this gap. Borrowing from Software Bill of Materials (SBOMs), we design SkillDepAnalyzer to capture natural-language dependency evidence and model skills as dependency-bearing artifacts. On the SKILL-DEP benchmark, SkillDepAnalyzer recovers skill metadata and dependency graphs accurately and comprehensively, substantially outperforming an LLM-based baseline and package-centric SBOM tools. Applying SkillDepAnalyzer to over 1.43 million skills, we obtain ASSCs and explore their structural diversity and security signals. We find four structural patterns: skill metadata is activation-ready but governance-poor; dependency graphs span skill, package, and service dependencies with concentrated reuse; recursive skill reuse expands dependency graphs and creates hidden package inventory; and skill dependency clusters form around related workflows. We also find that inspecting a skill alone misses security-relevant signals hiding in its dependencies. By analyzing ASSCs, we identify and report known malicious skills persisting in ASSCs to their developers. Based on these findings, we recommend typed dependency manifests, first-class dependency-cluster management, risk-warning audit commands for skill infrastructure maintainers, and lockfile-like records for skill developers.
| Subjects: | Software Engineering (cs.SE); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.01136 [cs.SE] |
| (or arXiv:2607.01136v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2607.01136 Focus to learn more arXiv-issued DOI via DataCite |
Submission history
From: Changguo Jia [view email][v1] Wed, 1 Jul 2026 16:21:49 UTC (312 KB)
Full-text links:
Access Paper:
-
View a PDF of the paper titled Skills Are Not Islands: Measuring Dependency and Risk in Agent Skill Supply Chains, by Changguo Jia and 3 other authors
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
cs.SE < prev | next >new | recent | 2026-07 Change to browse by: cs
cs.AI
References & Citations
export BibTeX citation Loading...BibTeX formatted citation
× Data provided by:Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, MediaCode, Data and Media Associated with this Article
alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) DemosDemos
Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related PapersRecommenders and Search Tools
Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) About arXivLabsarXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)Related items
FeaturedDeepSeek V4 Doubles Token Share on OpenRouter, Driven by Agentic Workloads
DeepSeek V4 在 OpenRouter 上的代币份额翻倍，智能体工作负载成主要驱动力
The rapid adoption of DeepSeek V4 signals a strong market preference for high-performance, low-cost inference models, especially as agentic use cases reshape AI deployment.
91
FeaturedEnabling Two Settings Triples ARC-AGI-3 Benchmark Scores
启用两项设置使ARC-AGI-3基准测试分数提升三倍
This finding is crucial! It shows that model performance depends not only on training but also on inference-time configuration. For AI engineers and researchers, this is a must-know optimization direction. Teams deploying models on resource-constrained devices in particular could find a low-cost way to boost capabilities.
91
FeaturedGPT-5.6: Fusing Frontier Intelligence with Frontier Efficiency
GPT-5.6：前沿智能与前沿效率的融合
This news is worth watching as it marks a shift from pure parameter scaling to efficiency competition. For AI developers and business decision-makers, GPT-5.6 means higher performance at lower cost, especially for long-context or agentic tasks previously hindered by inference expenses. OpenAI's move may redefine what 'frontier' means.
91
FeaturedLFM2.5-Encoders: New Models for Fast Long-Context Inference on CPU
LFM2.5-Encoder：面向CPU快速长上下文推理的新模型
This blog post catches my eye because it directly solves the pain point of running long-context LLMs on CPU. For teams or individual developers without high-end GPUs, the LFM2.5-Encoder series offers a ready-to-use open-source solution. Highly recommended for those working on RAG, document search, or code analysis—now a standard server can handle million-token documents.
91
FeaturedGPT-Red: OpenAI Automates Red Teaming via Self-Play to Boost Model Robustness
GPT-Red：OpenAI通过自我对弈实现AI红队自动化，提升模型鲁棒性
GPT-Red marks a milestone from human to automated red-teaming—essential for safety engineers and alignment researchers. While reducing cost, it also raises ethical questions about automated attacks, worth pondering for everyone in AI governance.
91
FeaturedOpenAI Launches GPT-5.5 Bio Bug Bounty Program
OpenAI推出GPT-5.5生物漏洞赏金计划
This program deserves attention as it pioneers the extension of AI bug bounty to biosecurity, offering a new research direction for the security community and a practical training ground for AI risk governance. It is particularly important for model safety researchers, biosecurity experts, and practitioners concerned about dual-use AI risks.
91
FeaturedvLLM Introduces Native-Speed Transformers Modeling Backend for Enhanced Inference Performance
vLLM推出原生速度Transformers建模后端，大幅提升推理性能
This update is critical for AI engineering teams: eliminating format conversion speeds up model iteration, and 2-5x speedup directly reduces GPU costs. If you're building LLM online services or batch inference, this is practically a must-read technical update.
90
FeaturedGoogle DeepMind Introduces SL2T, a Sign Language-to-Text Model for Deaf Users
Google DeepMind 发布手语转文本模型 SL2T，助力聋人用户
This news matters not only as a technical breakthrough but also as a sign that AI is beginning to address the needs of marginalized groups. For developers, the architecture and technical approach of SL2T merit deep study; for accessibility advocates, it's a key case for advancing industry standards.