A coding-agent skill that turns your agent into a security auditor. It orchestrates isolated agents through reconnaissance, coverage-led hunting, candidate validation, structured output, independent record verification, and target-neutral reporting.
This is the skill that seeded Cloudflare's vulnerability discovery harness, described in Build your own vulnerability harness. The harness grew into a multi-stage, fleet-wide system; this skill is the single-repo starting point it evolved from.
The skill runs a structured audit in six phases:
- Reconnaissance -- map architecture, trust boundaries, input surfaces, prior evidence, and deterministic coverage in
architecture.mdandcoverage-ledger.json. - Coverage-led hunting -- assign isolated hunters from ledger units, record their checks, and use coverage critics to find gaps.
- Candidate validation -- give every unique candidate to a fresh verifier that tries to disprove it.
- Structured output -- write
confirmed,needs_validation, andrejectedrecords tofindings.jsonand validate them againstreport-schema.json. - Independent record verification -- fresh agents verify final source claims. Material replacements receive another independent verifier.
- Target-neutral reporting -- derive
REPORT.md,FINDINGS-DETAIL.md, andNEEDS-VALIDATION.mdfrom the verified records and coverage ledger.
The parent runs validate-coverage-ledger.cjs after creating the ledger and after each later ledger update. It runs validate-findings.cjs in Phase 4 and again after every Phase 5 replacement.
The verdicts are distinct: confirmed has a complete source trace and bounded observed result, needs_validation has an exact unresolved fact and no severity, and rejected records a disproved candidate.
Multiple runs against the same repo are additive. The skill uses prior ledgers and findings to target gaps, revalidate changed source, and carry forward current-source evidence without treating stale or unresolved work as covered.
| File | Purpose |
|---|---|
SKILL.md |
Setup, core principles, platform terminology, workflow overview, and audit anti-patterns |
RECONNAISSANCE.md |
Phase 1 reconnaissance prompts and synthesis instructions |
HUNTING.md |
Phase 2 orchestration, hunting methodology, and validation rules |
ATTACK-CLASSES.md |
Core, wildcard, and obvious-things attack prompts |
MEMORY-SAFETY-AND-BINARY.md |
Memory-safety, binary, and kernel hunting classes for native targets |
AI-AND-LLM.md |
Prompt-injection, agent/tool, and output-handling hunting classes for LLM-backed targets |
WEB-PROTOCOL-AND-AUTH.md |
HTTP request-framing, cache, and authentication-protocol hunting classes for HTTP-protocol and auth targets |
CLIENT-SIDE.md |
DOM-injection, messaging-trust, UI-redress, and prototype-pollution hunting classes for client-side/browser targets |
SUPPLY-CHAIN-AND-RELEASE.md |
Dependency, CI, release, signing, update, plugin, and extension hunting classes |
CLOUD-AND-DEPLOYMENT.md |
IAM, infrastructure-as-code, container, serverless, ingress, and runtime-configuration hunting classes |
PROTOCOLS-RPC-AND-MESSAGING.md |
RPC, serialization, queue, broker, webhook, and streaming-protocol hunting classes |
RESOURCE-EXHAUSTION-AND-AVAILABILITY.md |
Shared resource, quota, queue, worker, and operator-spend hunting classes |
DATA-ISOLATION-AND-LIFECYCLE.md |
Tenant isolation, cache, search, export, backup, migration, deletion, and restore hunting classes |
DESKTOP-MOBILE-AND-LOCAL-IPC.md |
Native app, deep-link, webview, exported-component, helper, daemon, and local-IPC hunting classes |
VALIDATION-AND-REPORTING.md |
Phases 3–6 candidate validation, structured output, record verification, and reporting |
report-schema.json |
JSON schema for all three findings.json verdicts |
validate-findings.cjs |
Zero-dependency validator for findings.json in Phases 4 and 5 |
validate-findings.test.cjs |
Findings-validator tests and producer-compatible fixture checks |
validate-coverage-ledger.cjs |
Zero-dependency validator for coverage-ledger.json in Phases 1–5 |
validate-coverage-ledger.test.cjs |
Coverage-ledger validator tests |
Install the skill with the Skills CLI:
npx skills add https://github.com/cloudflare/security-audit-skill \
--skill security-auditUse --global for a user-level installation:
npx skills add https://github.com/cloudflare/security-audit-skill \
--skill security-audit \
--globalRun npx skills --help for agent-selection and non-interactive options.
Start your coding agent in (or pointed at) the codebase you want to audit, then ask it to do a security audit:
security audit this codebase
find security vulnerabilities in ./src
do a security review, output to ~/audits/my-project
The skill activates automatically when the request matches its trigger (security audit, find vulnerabilities, pen-test the code, etc.). A direct codebase audit or pen-test request uses full audit mode. Security questions and focused vulnerability work use guidance mode unless you request report artifacts. In full audit mode, an unspecified output directory defaults to ~/security-audit-skill/<repo-name>/run-<N>. The workflow writes inside the target repository only when you explicitly select a directory that version control ignores.
- A coding agent with a model that supports tool use and parallel sub-agents
- Node.js for the zero-dependency findings and coverage-ledger validators
- An OS-enforced sandbox for target-controlled builds, tests, processes, browsers, emulators, fuzzers, and fixtures. It must disable external networking, use a sanitized allowlisted environment, enforce resource limits, and allow writes only to assigned scratch paths. Without these controls, the workflow keeps the lead as
needs_validationinstead of executing target code.
- Only confirm established boundary failures. Keep a source-grounded blocked lead as
needs_validationwith its exact unresolved fact. - Adversarial validation. The agent that checks a finding is never the agent that found it.
- Severity requires impact. Likelihood x impact, not deviation from a checklist.
- Defense-in-depth gaps are not vulnerabilities. If Layer A prevents the attack, the absence of Layer B is a hardening note.
- Multiple runs improve coverage. In our test runs, a single run found roughly half of the vulnerabilities that repeated runs found in total.
Questions, feedback, or comparing notes on AI-driven security tooling: security-ai-research@cloudflare.com
MIT -- see LICENSE.