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An Analysis of Malicious Packages in Open-Source Software in the Wild

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arxiv 2404.04991 v3 pith:VXROSLL7 submitted 2024-04-07 cs.CR cs.SE

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

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The open-source software (OSS) ecosystem suffers from security threats caused by malware.However, OSS malware research has three limitations: a lack of high-quality datasets, a lack of malware diversity, and a lack of attack campaign contexts. In this paper, we first build the largest dataset of 24,356 malicious packages from online sources, then propose a knowledge graph to represent the OSS malware corpus and conduct malware analysis in the wild.Our main findings include (1) it is essential to collect malicious packages from various online sources because their data overlapping degrees are small;(2) despite the sheer volume of malicious packages, many reuse similar code, leading to a low diversity of malware;(3) only 28 malicious packages were repeatedly hidden via dependency libraries of 1,354 malicious packages, and dependency-hidden malware has a shorter active time;(4) security reports are the only reliable source for disclosing the malware-based context. Index Terms: Malicious Packages, Software Analysis

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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. ProfMalPlus: Agent-Coordinated Detection of Malicious NPM Packages via Static-Dynamic Analysis Synergy

    cs.SE 2026-07 conditional novelty 6.0 of 10

    ProfMalPlus detects malicious NPM packages by extracting security-relevant code slices from object-sensitive behavior graphs and having coordinated LLM agents judge, enrich, and localize the evidence.

  2. Wolves in the Repository: A Software Engineering Analysis of the XZ Utils Supply Chain Attack

    cs.SE 2025-04 conditional novelty 5.0 of 10

    A mixed-methods analysis of the XZ Utils attack shows the attacker weaponized routine software engineering practices, especially non-code contributions, to gain maintainer trust and hide malicious commits.

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