Large Scale Study of Orphan Vulnerabilities in the Software Supply Chain
The security of the software supply chain has become a critical issue in an era where the majority of software projects use open source software dependencies, exposing them to vulnerabilities in those dependencies. Awareness of this issue has led to the creation of dependency tracking tools that can identify and remediate such vulnerabilities. These tools rely on package manager metadata to identify dependencies, but open source developers often copy dependencies into their repositories manually without the use of a package manager. In order to understand the size and impact of this problem, we designed a large scale empirical study to investigate vulnerabilities propagated through copying of dependencies. Such vulnerabilities are called orphan vulnerabilities. We created a tool, VCAnalyzer, to find orphan vulnerabilities copied from an initial set of vulnerable files. Starting from an initial set of 3,615 vulnerable files from the CVEfixes dataset, we constructed a dataset of more than three million orphan vulnerabilities found in over seven hundred thousand open source projects. We found that 83.4% of the vulnerable files from the CVEfixes dataset were copied at least once. A majority (59.3%) of copied vulnerable files contained C source code. Only 1.3% of orphan vulnerabilities were ever remediated. Remediation took 469 days on average, with half of vulnerabilities in active projects requiring more than three years to fix. Our findings demonstrate that the number of orphan vulnerabilities not trackable by dependency managers is large and point to a need for improving how software supply chain tools identify dependencies. We make our VCAnalyzer tool and our dataset publicly available.
Fri 8 DecDisplayed time zone: Pacific Time (US & Canada) change
11:00 - 12:30 | Bugs, smells, and vulnerabilitiesPROMISE 2023 at Foothill G Chair(s): Miroslaw Staron University of Gothenburg | ||
11:00 30mPaper | BuggIn: Automatic Intrinsic Bugs Classification Model using NLP and ML PROMISE 2023 Pragya Bhandari University of British Columbia, Gema Rodríguez-Pérez University of British Columbia (UBC) DOI | ||
11:30 30mPaper | Do Developers Fix Continuous Integration Smells? PROMISE 2023 Ayberk Yaşa Bilkent University, Ege Ergül Bilkent University, Eray Tüzün Bilkent University, Hakan Erdogmus Carnegie Mellon University DOI | ||
12:00 30mPaper | Large Scale Study of Orphan Vulnerabilities in the Software Supply Chain PROMISE 2023 David Reid University of Tennessee, Kristiina Rahkema University of Tartu, James Walden Northern Kentucky University DOI |