Empirical analysis of security vulnerabilities in Python packages

Mahmoud Alfadel

doi:10.1007/s10664-022-10278-4

2023-03-25

PRE

Mahmoud Alfadel *

Diego Elias Costa

Emad Shihab

DOI：10.1007/s10664-022-10278-4

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摘要

En 中文

Software ecosystems play an important role in modern software development, providing an open platform of reusable packages that speed up and facilitate development tasks. However, this level of code reusability supported by software ecosystems also makes the discovery of security vulnerabilities much more difficult, as software systems depend on an increasingly high number of packages. Recently, security vulnerabilities in the npm ecosystem, the ecosystem of Node.js packages, have been studied in the literature. As different software ecosystems embody different programming languages and particularities, we argue that it is also important to study other popular programming languages to build stronger empirical evidence about vulnerabilities in software ecosystems. In this paper, we present an empirical study of 1,396 vulnerability reports affecting 698 Python packages in the Python ecosystem (PyPi). In particular, we study the propagation and life span of security vulnerabilities, accounting for how long they take to be discovered and fixed. In addition, vulnerabilities in packages may affect software projects that depend on them (dependent projects), making them vulnerable too. We study a set of 2,224 GitHub Python projects, to better understand the prevalence of vulnerabilities in their dependencies and how fast it takes to update them. Our findings show that the discovered vulnerabilities in Python packages are increasing over time, and they take more than 3 years to be discovered. A large portion of these vulnerabilities (40.86%) are only fixed after being publicly announced, giving ample time for attackers exploitation. Moreover, we find that more than half of the dependent projects rely on at least one vulnerable package, taking a considerably long time (7 months) to update to a non-vulnerable version. We find similarities in some characteristics of vulnerabilities in PyPi and npm and divergences that can be attributed to specific PyPi policies. By leveraging our findings, we provide a series of implications that can help the security of software ecosystems by improving the process of discovering, fixing and managing package vulnerabilities.

Keyword:

Python

PyPi

Packages

Vulnerabilities

Empirical studies