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Related Experiment Video

Updated: Sep 12, 2025

gP2S, an Information Management System for CryoEM Experiments

gP2S, an Information Management System for CryoEM Experiments

Published on: June 10, 2021

A dataset on vulnerabilities affecting dependencies in software package managers.

A Germán Márquez¹, Ángel Jesús Varela-Vaca¹, María Teresa Gómez López¹

¹i3US and University of Seville, Dept. of Computer Languages and Systems, IDEA Research Group, Seville, Spain.

|August 8, 2025

Summary

This study introduces a dataset mapping software dependency vulnerabilities across NPM, PyPI, Cargo, and RubyGems. It reveals millions of package versions affected, highlighting critical software supply chain security risks.

Keywords:

Cargo NPM Package PyPI RubyGems Security Vulnerability

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Area of Science:

Computer Science
Software Engineering
Cybersecurity

Background:

Third-party dependencies are integral to modern software development.
Their use introduces significant security risks to the software supply chain.
Existing vulnerability data needs structured integration for effective analysis.

Purpose of the Study:

To create a comprehensive dataset mapping vulnerabilities in major package managers.
To enable automated monitoring and analysis of vulnerable software dependencies.
To support the development of tools for enhanced software supply chain security.

Main Methods:

Extracted dependency and version data from NPM, PyPI, Cargo, and RubyGems.
Correlated package data with vulnerability reports from Open Source Vulnerabilities (OSV).
Structured the dataset with millions of package versions and thousands of known vulnerabilities.

Main Results:

Dataset covers 4,437,679 unique packages and 60,950,846 versions.
Identified 270,430 known vulnerabilities linked to specific package versions.
Found significant percentages of vulnerable versions across PyPI (0.42%), RubyGems (7.5%), Cargo (3.91%), and NPM (6.93%).

Conclusions:

The dataset provides a foundation for improved software supply chain security.
Enables early vulnerability detection and mitigation strategy development.
Promotes more secure software ecosystems through data-driven security analytics.