mlsanityData to 14 Oct 2026

What are Weak Links in the npm Supply Chain?

arXiv 2112.10165 ↗First posted 19 Dec 2021cs.CR cs.CY cs.SEcited by 16

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Abstract (arXiv)

Modern software development frequently uses third-party packages, raising the concern of supply chain security attacks. Many attackers target popular package managers, like npm, and their users with supply chain attacks. In 2021 there was a 650% year-on-year growth in security attacks by exploiting Open Source Software's supply chain. Proactive approaches are needed to predict package vulnerability to high-risk supply chain attacks. The goal of this work is to help software developers and security specialists in measuring npm supply chain weak link signals to prevent future supply chain attacks by empirically studying npm package metadata. In this paper, we analyzed the metadata of 1.63 million JavaScript npm packages. We propose six signals of security weaknesses in a software supply chain, such as the presence of install scripts, maintainer accounts associated with an expired email domain, and inactive packages with inactive maintainers. One of our case studies identified 11 malicious packages from the install scripts signal. We also found 2,818 maintainer email addresses associated with expired domains, allowing an attacker to hijack 8,494 packages by taking over the npm accounts. We obtained feedback on our weak link signals through a survey responded to by 470 npm package developers. The majority of the developers supported three out of our six proposed weak link signals. The developers also indicated that they would want to be notified about weak links signals before using third-party packages. Additionally, we discussed eight new signals suggested by package developers.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 19 Dec 2021.

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Citing papers per year: 2022: 1; 2023: 2; 2024: 2; 2025: 6; 2026: 5.

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  1. 2023An Empirical Study of Pre-Trained Model Reuse in the Hugging Face Deep Learning Model Registrycited by 47
  2. 2026How Your Credentials Are Leaked by LLM Agent Skills: An Empirical Studycited by 19
  3. 2024Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answerscited by 13
  4. 2023An Empirical Study on Using Large Language Models to Analyze Software Supply Chain Security Failurescited by 11
  5. 2024Leveraging Large Language Models to Detect npm Malicious Packagescited by 6
  6. 2025A First Look at the Security Issues in the Model Context Protocol Ecosystemcited by 5
  7. 2026A Deterministic Control Plane for LLM Coding Agentscited by 3
  8. 2025SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deploymentcited by 2
  9. 2025Automatically Generating Rules of Malicious Software Packages via Large Language Modelcited by 2
  10. 2026Broken on Arrival: Silently Defective LLM Artifacts in Public Model Registries and How to Catch Themcited by 2
  11. 2022Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentationcited by 1
  12. 2025Investigating Security Implications of Automatically Generated Code on the Software Supply Chaincited by 1
4 more
  1. 2025Leveraging Code Cohesion Analysis to Identify Source Code Supply Chain Attackscited by 0
  2. 2025Learning to Triage Vulnerability Reports from Program Analysis: An Empirical Study in Node.jscited by 0
  3. 2026CCCE: A Continuous Code Calibration Engine for Autonomous Enterprise Codebase Maintenance via Knowledge Graph Traversal and Adaptive Decision Gatingcited by 0
  4. 2026A Large-Scale Empirical Study of Quality Assurance Practices and Gaps in AI Agentscited by 0