eyeballvul: a future-proof benchmark for vulnerability detection in the wild

• URL: http://arxiv.org/abs/2407.08708v2
• Date: Sat, 13 Jul 2024 10:44:58 GMT
• Title: eyeballvul: a future-proof benchmark for vulnerability detection in the wild
• Authors: Timothee Chauvin,
• Abstract summary: eyeballvul is a benchmark designed to test the vulnerability detection capabilities of language models at scale. It is sourced and updated weekly from the stream of published vulnerabilities in open-source repositories. eyeballvul contains 24,000+ vulnerabilities across 6,000+ revisions and 5,000+ repositories, and is around 55GB in size.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long contexts of recent LLMs have enabled a new use case: asking models to find security vulnerabilities in entire codebases. To evaluate model performance on this task, we introduce eyeballvul: a benchmark designed to test the vulnerability detection capabilities of language models at scale, that is sourced and updated weekly from the stream of published vulnerabilities in open-source repositories. The benchmark consists of a list of revisions in different repositories, each associated with the list of known vulnerabilities present at that revision. An LLM-based scorer is used to compare the list of possible vulnerabilities returned by a model to the list of known vulnerabilities for each revision. As of July 2024, eyeballvul contains 24,000+ vulnerabilities across 6,000+ revisions and 5,000+ repositories, and is around 55GB in size.

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