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SpellBound: Defending Against Package Typosquatting

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arxiv 2003.03471 v1 pith:P3SDFQAJ submitted 2020-03-06 cs.SE cs.CR

Matthew Taylor , Ruturaj K. Vaidya , Drew Davidson , Lorenzo De Carli , Vaibhav Rastogi This is my paper · ORCID
classification cs.SEcs.CR
keywords packagetyposquattingspellboundpackagescasesdeveloperspopularpypi
verification ladder T0 review T1 audit T2 compute T3 formal
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Package managers for software repositories based on a single programming language are very common. Examples include npm (JavaScript), and PyPI (Python). These tools encourage code reuse, making it trivial for developers to import external packages. Unfortunately, repositories' size and the ease with which packages can be published facilitates the practice of typosquatting: the uploading of a package with name similar to that of a highly popular package, typically with the aim of capturing some of the popular package's installs. Typosquatting has serious negative implications, resulting in developers importing malicious packages, or -- as we show -- code clones which do not incorporate recent security updates. In order to tackle this problem, we present SpellBound, a tool for identifying and reporting potentially erroneous imports to developers. SpellBound implements a novel typosquatting detection technique, based on an in-depth analysis of npm and PyPI. Our technique leverages a model of lexical similarity between names, and further incorporates the notion of package popularity. This approach flags cases where unknown/scarcely used packages would be installed in place of popular ones with similar names, before installation occurs. We evaluated SpellBound on both npm and PyPI, with encouraging results: SpellBound flags typosquatting cases while generating limited warnings (0.5% of total package installs), and low overhead (only 2.5% of package install time). Furthermore, SpellBound allowed us to confirm known cases of typosquatting and discover one high-profile, unknown case of typosquatting that resulted in a package takedown by the npm security team.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 9 citations worldwide. Full citation record

  1. LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection

    cs.CR 2026-07 conditional novelty 5.5 of 10

    H2GLM combines LLM-inferred function roles with hierarchical heterogeneous GNN message passing to detect and localize malicious Python packages more accurately than prior ML, graph, and LLM baselines.

  2. A Machine Learning-Based Approach For Detecting Malicious PyPI Packages

    cs.SE 2024-12 conditional novelty 5.0 of 10

    A stacking ensemble classifier using static features, including a new vocabulary-based text feature, detects malicious PyPI packages with a claimed F1 of 94 percent, though the per-class malicious F1 is 88 percent.

  3. A Study of Malware Prevention in Linux Distributions

    cs.CR 2024-11 conditional novelty 5.0 of 10

    Linux distributions mostly rely on reproducible builds and signing, and current open-source malware scanners perform poorly on a new benchmark of malicious Linux packages.

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