Characterizing Packages for Vulnerability Prediction
Bibliographic record
Abstract
Modern software development relies heavily on the use of external libraries and packages as software reuse provides benefits, such as reduced time to market and lower development cost. However, these libraries often come with their own set of direct and indirect dependencies which could introduce vulnerabilities, compromising the security of end users. Prior work shows that developers may remain unaware of these vulnerabilities until a security incident that exploits them occurs, leading to potential consequences for data privacy. Therefore, it is essential for developers to have the ability, before committing time to a project, to understand whether the external libraries and packages they intend to use may induce vulnerabilities, and how that might happen. In our work, we use the dataset made available by the Goblin framework to identify and evaluate salient features for predicting the vulnerability profile of software packages. We use these features to build classifiers for predicting whether or not a dependency-related vulnerability will occur within 3, 6, or 12 months. Our approach proves to be effective, achieving F1-scores of 0.74, 0.79 and 0.86 in the 3, 6, and 12 month contexts respectively. Providing timely vulnerability information could help developers identify potential security weaknesses before deploying a package to production, thereby minimizing the risk of security incidents.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".