Agentic AI security tool that applies proactive, attacker-first analysis directly to source code.
Capital One open-sources VulnHunter: agentic AI SASTSASTStatic application security testing, analyzing source code or compiled code for vulnerabilities without running the application. It helps developers find problems before deployment. with attacker-first analysis, falsification engine, and automated fix-verify loop.
└──▷ GET THIS VERSION
$ git clone --branch v0.1.0 https://github.com/capitalone/VulnHunter.git # already have the repo? check out this version: $ git checkout v0.1.0
$ claude --model opus --add-dir ~/.claude/skills/vulnhunt --add-dir ~/.claude/skills/vulnhunt/phases # Then inside the Claude Code session: /vulnhunt
$ claude --model opus --add-dir ~/.claude/skills/vulnhunt-fix-verify \ --add-dir ~/.claude/skills/vulnhunt-fix-verify/phases # Then inside the Claude Code session: /vulnhunt-fix-verify repo=<abs_path> report=<abs_path> fixed=VULN-001,... out=<abs_path>
$ cd harness python -m local_harness.batch.run scan --resume python -m local_harness.batch.run collect
- Adds
/vulnhuntClaude Code skill: entry-point-forward scanner that maps attacker paths to dangerous sinks and filters findings through a multi-stage falsification pipeline (Recon → Parallel Hunt → Adversarial Disprove → Capability Filter). - Adds
/vulnhunter-fixskill: test-driven remediation loop that writes an exploit demo, creates a failing security test (RED), implements the fix (GREEN), verifies the exploit is blocked, and opens a reviewable PR. - Adds
/vulnhunt-fix-verifyskill: independent, read-only agent that validates whether a finding was successfully remediated and emits a per-finding verdict. - Adds
vulnhunter-agent/headless runtime: clones targets, runs/vulnhunt, publishes results, and opens GitHub issues for confirmed vulnerabilities — suitable for CI/CD pipelines. - Adds
harness/batch-scanning and benchmarking tooling: manage a repo list, resume interrupted scans, collect findings centrally, and evaluate detection accuracy against a known-vulnerable corpus with LLMLLMA large language model, a neural network trained on large amounts of text to recognize patterns and generate language. Applications use it for tasks such as answering, summarizing and writing code.-judge scoring.
5 details in Everything