Unlike general-purpose LLMs, this model has been instruction-tuned to think like a security analyst. It is designed to ingest raw technical vulnerability data (such as CVE descriptions or service banners) and output structured, actionable intelligence for penetration testing reports.
This model serves as a "Decision & Analysis" node as part of broader autonomous agent architecture. Its fine-tuning focused on three core competencies:
As part of our PAIC 2025 architecture, this model sits downstream from the our recon, scanning, and vulnerability identification agents. It receives aggregated inventory data and provides the high-level reasoning required to map technical flaws to actual attack paths, along with contributing to reports provided to sysadmins and more technical users.
The model was fine-tuned on a synthetic dataset of
3,660 high-quality examples derived from the official
CVEListV5 registry. You can access the specific dataset used for this fine-tuning here:
aavhawkeye/cve-red-team-v1.
