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Security Issues in Language-based Software Ecosystems

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arxiv 1903.02613 v2 pith:XQTHHAWO submitted 2019-03-06 cs.CR

classification cs.CR
keywords ecosystemsissuespackagessecurityattackscodesoftwareaspects
verification ladder T0 review T1 audit T2 compute T3 formal
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Language-based ecosystems (LBE), i.e., software ecosystems based on a single programming language, are very common. Examples include the npm ecosystem for JavaScript, and PyPI for Python. These environments encourage code reuse between packages, and incorporate utilities - package managers - for automatically resolving dependencies. However, the same aspects that make these systems popular - ease of publishing code and importing external code - also create novel security issues, which have so far seen little study. We present an a systematic study of security issues that plague LBEs. These issues are inherent to the ways these ecosystems work and cannot be resolved by fixing software vulnerabilities in either the packages or the utilities, e.g., package manager tools, that build these ecosystems. We systematically characterize recent security attacks from various aspects, including attack strategies, vectors, and goals. Our characterization and in-depth analysis of npm and PyPI ecosystems, which represent the largest LBEs, covering nearly one million packages indicates that these ecosystems make an opportune environment for attackers to incorporate stealthy attacks. Overall, we argue that (i) fully automated detection of malicious packages is likely to be unfeasible; however (ii) tools and metrics that help developers assess the risk of including external dependencies would go a long way toward preventing attacks.

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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. Threadbox: Sandboxing for Modular Security

    cs.CR 2025-06 conditional novelty 6.0 of 10

    Threadbox is a thread-granular, non-inheriting Linux sandbox with Pledge-like promises and function-level annotation, demonstrated on Flask, magic-wormhole, and a PDF reader.

  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.

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