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What are Weak Links in the npm Supply Chain?

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arxiv 2112.10165 v2 pith:C5LBB2M2 submitted 2021-12-19 cs.CR cs.CYcs.SE

classification cs.CRcs.CYcs.SE
keywords chainsupplypackagessignalsattacksdeveloperspackageweak
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PoCGen: Generating Proof-of-Concept Exploits for Vulnerabilities in Npm Packages

    cs.CR 2025-06 conditional novelty 7.0 of 10

    PoCGen combines LLM-based exploit generation with static taint analysis and dynamic validation to produce proof-of-concept exploits for 77% of 560 npm vulnerabilities in the SecBench.js dataset.

  2. Automatically Generating Rules of Malicious Software Packages via Large Language Model

    cs.SE 2025-04 conditional novelty 6.0 of 10

    RuleLLM automatically generates YARA and Semgrep rules from malware metadata and code, reporting 85.2% precision and 91.8% recall on its 1,633-package corpus.

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