An empirical study on vulnerability disclosure management of open source software systems
Bibliographic record
Abstract
Vulnerability disclosure is critical for ensuring the security and reliability of open source software (OSS). However, in practice, many vulnerabilities are reported and discussed on public platforms before being formally disclosed, posing significant risks to vulnerability management. Inadequate vulnerability disclosure can expose users to security threats and severely impact the stability and reliability of software systems. For example, prior work shows that over 21% of CVEs are publicly discussed before a patch is released. Despite its importance, we still lack clarity on the vulnerability disclosure practices adopted by open source communities and the preferences of practitioners regarding vulnerability management. To fill this gap, we analyzed the vulnerability disclosure practices of 8,073 OSS projects spanning from 2017 to 2023. We then conducted an empirical study by surveying practitioners about their preferences and recommendations in vulnerability disclosure management. Finally, we compared the survey results with the actual vulnerability practice observed within the OSS projects. Our results show that while over 80% of practitioners support Coordinated Vulnerability Disclosure (CVD), only 55% of vulnerabilities conform to CVD in practice. Although only 20% of practitioners advocate discussions before disclosure, 42% of vulnerabilities are discussed in issue reports before their disclosure. This study reveals the vulnerability management practices in OSS, provides valuable guidance to OSS owners, and highlights potential directions to improve the security of OSS platforms.
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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 | 0.021 | 0.144 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".