"I wasn't sure if this is indeed a security risk": Data-driven Understanding of Security Issue Reporting in GitHub Repositories of Open Source npm Packages

• URL: http://arxiv.org/abs/2506.07728v1
• Date: Mon, 09 Jun 2025 13:11:35 GMT
• Title: "I wasn't sure if this is indeed a security risk": Data-driven Understanding of Security Issue Reporting in GitHub Repositories of Open Source npm Packages
• Authors: Rajdeep Ghosh, Shiladitya De, Mainack Mondal,
• Abstract summary: This work collected 10,907,467 issues reported across GitHub repositories of 45,466 diverse npm packages.<n>We found that the tags associated with these issues indicate the existence of only 0.13% security-related issues.<n>Our approach of manual analysis followed by developing high accuracy machine learning models identify 1,617,738 security-related issues which are not tagged as security-related.
• Score: 8.360992461585308
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The npm (Node Package Manager) ecosystem is the most important package manager for JavaScript development with millions of users. Consequently, a plethora of earlier work investigated how vulnerability reporting, patch propagation, and in general detection as well as resolution of security issues in such ecosystems can be facilitated. However, understanding the ground reality of security-related issue reporting by users (and bots) in npm-along with the associated challenges has been relatively less explored at scale. In this work, we bridge this gap by collecting 10,907,467 issues reported across GitHub repositories of 45,466 diverse npm packages. We found that the tags associated with these issues indicate the existence of only 0.13% security-related issues. However, our approach of manual analysis followed by developing high accuracy machine learning models identify 1,617,738 security-related issues which are not tagged as security-related (14.8% of all issues) as well as 4,461,934 comments made on these issues. We found that the bots which are in wide use today might not be sufficient for either detecting or offering assistance. Furthermore, our analysis of user-developer interaction data hints that many user-reported security issues might not be addressed by developers-they are not tagged as security-related issues and might be closed without valid justification. Consequently, a correlation analysis hints that the developers quickly handle security issues with known solutions (e.g., corresponding to CVE). However, security issues without such known solutions (even with reproducible code) might not be resolved. Our findings offer actionable insights for improving security management in open-source ecosystems, highlighting the need for smarter tools and better collaboration. The data and code for this work is available at https://doi.org/10.5281/zenodo.15614029

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