Cloudflare open-sources security-audit-skill for AI coding agents
Cloudflare released its internal security audit framework as an open-source MIT-licensed skill that turns AI coding agents into structured security auditors using six-phase methodology with adversarial validation.
Summary
Cloudflare published security-audit-skill, the single-repository origin of its internal fleet-wide vulnerability discovery harness. The tool installs via a Skills CLI and orchestrates isolated AI agents through reconnaissance, coverage-led hunting, candidate validation, structured output, independent verification, and target-neutral reporting. It requires a coding agent with tool-use and parallel sub-agent support, plus Node.js for validators and an OS-enforced sandbox for target-controlled execution.
Six-phase audit structure
- Reconnaissance: Maps architecture, trust boundaries, input surfaces into
architecture.mdandcoverage-ledger.json - Coverage-led hunting: Isolated hunters assigned per ledger unit; coverage critics identify gaps
- Adversarial validation: Every finding given to a fresh verifier that attempts disproof; material replacements get another independent verifier
- Three verdict classes:
confirmed(complete source trace),needs_validation(exact unresolved fact, no severity),rejected(disproved candidate) - Additive runs: Prior ledgers and findings target gaps, revalidate changed source, carry forward current evidence
- Sandbox requirement: OS-enforced sandbox with disabled external networking, sanitized environment, resource limits, scratch-path-only writes required for target-controlled execution
Why it matters
The release provides a reproducible, machine-readable alternative to ad-hoc AI security prompts, with explicit design constraints that reduce false positives by requiring impact-demonstrated severity and adversarial verification rather than checklist deviation.
Original reference: github.com →