PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python

2025-07-23

AI Summary

Python’s software supply chain heavily relies on the PyPI ecosystem, yet its complex, transitive dependency structure facilitates widespread vulnerability propagation; existing studies lack systematic, quantitative analysis of the prevalence of vulnerable dependencies. Method: We propose PyPitfall, the first framework to construct a complete dependency graph for all 378,573 PyPI packages, perform semantic version-range resolution, and precisely match dependencies against the CVE database to automate vulnerability propagation path analysis. Contribution/Results: Our analysis identifies 4,655 packages with explicit dependencies on known vulnerable versions and 141,044 packages that indirectly include such versions due to permissive version constraints. These findings quantify the scale of systemic security risks arising from dependency mismanagement in the Python ecosystem, providing the first large-scale empirical evidence and methodological foundation for software supply chain security governance.

Technology Category

• Reasoning under Uncertainty: Probabilistic Programming
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Python software development heavily relies on third-party packages. Direct and transitive dependencies create a labyrinth of software supply chains. While it is convenient to reuse code, vulnerabilities within these dependency chains can propagate through dependencies, potentially affecting down-stream packages and applications. PyPI, the official Python package repository, hosts many packages and lacks a comprehensive analysis of the prevalence of vulnerable dependencies. This paper introduces PyPitfall, a quantitative analysis of vulnerable dependencies across the PyPI ecosystem. We analyzed the dependency structures of 378,573 PyPI packages and identified 4,655 packages that explicitly require at least one known-vulnerable version and 141,044 packages that permit vulnerable versions within specified ranges. By characterizing the ecosystem-wide dependency landscape and the security impact of transitive dependencies, we aim to raise awareness of Python software supply chain security.

Problem

• Analyzing vulnerable dependencies in Python PyPI packages
• Identifying security risks from transitive dependency chains
• Quantifying ecosystem-wide software supply chain vulnerabilities

Innovation

• Analyzed dependency structures of PyPI packages
• Identified packages with known-vulnerable versions
• Characterized ecosystem-wide security impact