VinJ: An Automated Tool for Large-Scale Software Vulnerability Data Generation
Authors: 
Yu Nong, Haoran Yang, Feng Chen, 
Haipeng CaiAuthors Info & Claims
Pages 567 - 571
Abstract
We present VinJ, an efficient automated tool for large-scale diverse vulnerability data generation. VinJ automatically generates vulnerability data by injecting vulnerabilities into given programs, based on knowledge learned from existing vulnerability data. VinJ is able to generate diverse vulnerability data covering 18 CWEs with 69% success rate and generate 686k vulnerability samples in 74 hours (i.e., 0.4 seconds per sample), indicating it is efficient. The generated data is able to improve existing DL-based vulnerability detection, localization, and repair models significantly. The demo video of VinJ can be found at https://youtu.be/-oKoUqBbxD4. The tool website can be found at https://github.com/NewGillig/VInj. We also release the generated large-scale vulnerability dataset, which can be found at https://zenodo.org/records/10574446.
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Published: 10 July 2024
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FSE '24
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FSE '24: 32nd ACM International Conference on the Foundations of Software Engineering
July 15 - 19, 2024
Porto de Galinhas, Brazil
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