Empirical Analysis of Security Vulnerabilities in Python Packages
Bibliographic record
Abstract
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 embodied 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 550 vulnerability reports affecting 252 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. Our findings show that the discovered vulnerabilities in Python packages are increasing over time, and they take more than 3 years to be discovered. The majority of these vulnerabilities (50.55%) are only fixed after being publicly announced, giving ample time for attackers exploitation. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".