Mono reports that 31% of MegaVul patches are non-security and about 16.7% of CVEs are 'undecidable', while its added context raises LLM vulnerability detection F1 by up to 15%.
MegaVul: A C/C++ Vulnerability Dataset with Comprehensive Code Representation
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We constructed a newly large-scale and comprehensive C/C++ vulnerability dataset named MegaVul by crawling the Common Vulnerabilities and Exposures (CVE) database and CVE-related open-source projects. Specifically, we collected all crawlable descriptive information of the vulnerabilities from the CVE database and extracted all vulnerability-related code changes from 28 Git-based websites. We adopt advanced tools to ensure the extracted code integrality and enrich the code with four different transformed representations. In total, MegaVul contains 17,380 vulnerabilities collected from 992 open-source repositories spanning 169 different vulnerability types disclosed from January 2006 to October 2023. Thus, MegaVul can be used for a variety of software security-related tasks including detecting vulnerabilities and assessing vulnerability severity. All information is stored in the JSON format for easy usage. MegaVul is publicly available on GitHub and will be continuously updated. It can be easily extended to other programming languages.
citation-role summary
citation-polarity summary
fields
cs.CR 1years
2025 1verdicts
CONDITIONAL 1roles
dataset 1polarities
use dataset 1representative citing papers
citing papers explorer
-
Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond
Mono reports that 31% of MegaVul patches are non-security and about 16.7% of CVEs are 'undecidable', while its added context raises LLM vulnerability detection F1 by up to 15%.