VulnHunter: Agentic AI security scanner (fork)

VulnHunter is an open-source agentic AI security tool that applies attacker-first analysis to source code: instead of flagging suspicious patterns like passive SAST scanners, it starts at attacker-accessible entry points and reasons forward to determine whether an attacker can truly break through, then runs a falsification engine designed to disprove its own findings. This maintained fork of Capital One's VulnHunter (Apache-2.0) focuses on harness portability, sandboxed containerized exploit validation, and measured-impact PoCs, replacing Claude Code-specific skills with portable prompt files and adding a no-guess installer and an unattended operator skill. It ships three composable skills—vulnhunt (hunt), vulnhunter-fix (test-first remediation that writes an exploit demo, a failing test, the fix, and a PR), and vulnhunt-fix-verify (a read-only verifier). Benchmarking six scans of one production Go commit across five harness/model stacks showed a 14× spread in confirmed findings, 42/42 findings with passing executable exploit tests, and 55% of candidates eliminated by adversarial verification. The repo carries a dual-use caution, requires users to supply their own model access, and includes a headless runtime agent, a batch/benchmark harness, and a vh CLI. 

https://github.com/nealbridges/VulnHunter

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