security-audit-cloudflare

Audit codebases for exploitable vulnerabilities using multi-phase parallel agent workflows.

Updated Aug 20, 2026
One-click install
npx skills add https://github.com/bittlinkm/claude-setup --skill security-audit-cloudflare-bittlinkm
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: security-audit-cloudflare
Source: https://github.com/bittlinkm/claude-setup/tree/main/skills/security-audit-cloudflare
Command: npx skills add https://github.com/bittlinkm/claude-setup --skill security-audit-cloudflare-bittlinkm

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Manual security reviews miss subtle bugs and automated scanners only catch known patterns. This Skill orchestrates a structured, multi-phase security audit that finds exploitable vulnerabilities with real impact — including business logic flaws, chained attacks, and trust-boundary violations that scanners cannot detect. ## Core Features & Use Cases - Six-phase audit workflow: reconnaissance, parallel vulnerability hunting, adversarial validation, reporting, schema-validated structured output, and independent verification of every finding. - Domain-specific attack classes: dedicated hunting guides for web apps, memory-safety/binary/kernel targets, AI/LLM agents, HTTP protocol and auth (JWT, OAuth, SAML), and client-side browser code. - Machine-readable results: produces REPORT.md, FINDINGS-DETAIL.md, and a schema-validated findings.json with traces, severities, and remediations. - Use Case: Point it at a web API repository before a release; it maps trust boundaries, launches parallel hunting agents across injection, access control, and business logic classes, then returns a report containing only confirmed, exploitable findings with concrete attack scenarios. ## Quick Start Run a security audit on the codebase in the current directory and write the report to a new output folder.

Frequently Asked Questions about security-audit-cloudflare

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run a security audit on my codebase with AI agents?

Point the audit at your repository and it runs six phases: reconnaissance to map trust boundaries, parallel hunting agents per attack class, adversarial validation, report writing, schema-validated JSON output, and independent verification of every finding.

What vulnerability types does a security audit skill cover?

It covers injection, access control, business logic, cryptography, request smuggling, JWT/OAuth/SAML defects, DOM XSS, prototype pollution, memory safety, kernel bugs, and prompt injection in AI agents, plus a wildcard class for unconventional bugs.

Can it audit AI agents and LLM applications for prompt injection?

Yes, a dedicated AI/LLM hunting guide covers indirect prompt injection, tool-argument injection, excessive agency, MCP trust inheritance, and cross-tenant context bleed. Findings must cross a real trust boundary to be reported.

Does the audit report theoretical or only confirmed vulnerabilities?

Only confirmed findings are reported. Each must include a concrete attack scenario, pass an adversarial validation agent, conform to a JSON schema, and survive independent verification against the actual source code.

What output formats does the security audit produce?

It writes REPORT.md with an executive summary and findings table, FINDINGS-DETAIL.md with full data flows for medium-and-above findings, and findings.json validated against a strict schema with traces, severities, and remediations.

Why should I run the security audit multiple times on the same repo?

Each run explores different code paths, and a single run typically finds about half the total vulnerabilities. The skill reads prior findings.json files to skip known issues and target coverage gaps on subsequent runs.