Research reveals critical flaws in datasets used to train LLMs for security vulnerability detection and repair, undermining research validity.

• Many security vulnerability datasets suffer from high duplication rates
• Models trained on these datasets produce incorrect results and misleading evaluations
• Poor dataset quality creates a false sense of progress in vulnerability detection capabilities
• Rigorous dataset validation is essential for trustworthy security applications of LLMs

This finding matters because effective vulnerability detection tools are crucial for cybersecurity, but they can only be as good as the data they're trained on. Before deploying LLM-based security solutions, organizations must verify the quality of the underlying training datasets.

R+R: Security Vulnerability Dataset Quality Is Critical