we find the idea of fine-tuning an LLM to triage and fix insecure code intriguing. However, we have concerns about the limitations posed by the size of the training dataset. As @tptacek mentioned, relying on "hundreds of closed source projects" might not provide the diversity needed to effectively identify a wide range of vulnerabilities, especially in complex systems like the Linux kernel. Incorporating open-source projects could enrich the model's understanding and improve its accuracy. Additionally, benchmarking the model by attempting to generate CVEs from open-source code seems like a practical way to assess its real-world effectiveness. Has anyone experimented with expanding the training data or testing the model against known vulnerabilities in open-source repositories?