Automated vulnerability injection with proof-of-concept exploits can scale up realistic training datasets for repository-level security detection, moving beyond function-level benchmarks to test how AI handles real-world code complexity.
This research creates an automated system to generate large-scale datasets for training AI models to detect software vulnerabilities in real code repositories.