AI-Assisted Vulnerability Discovery

AI already found a 20-year-old buffer overflow in OpenSSL. It found 22 Firefox vulnerabilities in two weeks. It discovered a critical use-after-free in wolfSSL - a target with years of prior security testing - in the first hour of automated fuzzing. If you are still seeding by hand and triaging by eye, you are not behind on a tool. You are behind on a method.

This book gives you the complete, practical workflow that experienced security researchers are actually using in 2026 - not the academic version, not the vendor pitch, but the full pipeline from attack surface mapping through CVE submission. You will build a working AI-assisted vulnerability discovery lab using production-grade tools: AFL++, libFuzzer, LLM seed generation, automated crash triage, and a disclosure process that maintainers actually respond to. The framework is built around the DISCOVER Loop - a six-phase method that every case study in this book follows and that transfers to any target you will encounter.

What you will be able to do when you finish this book:

- Build a working LLM-assisted fuzzing pipeline from scratch using AFL++, libFuzzer, or Honggfuzz
- Generate AI seed corpora that reach the edge-case code where bugs actually live
- Triage hundreds of crash outputs to confirmed unique candidates without manually reading every one
- Identify attack surfaces in unfamiliar codebases in under an hour using LLM-assisted analysis
- Target network protocols, binary formats, and web APIs using AI-adapted fuzzing techniques
- Perform root cause analysis on sanitizer crash reports in 30-60 minutes with LLM assistance
- Develop a minimized, reproducible PoC from a fuzzer-found crash
- Build a Python orchestration loop that runs the full discovery pipeline autonomously
- Submit responsible disclosure reports that get acknowledged within 72 hours
- Navigate the legal framework around authorized security research without unnecessary exposure

Every technique in this book has been applied to real software. Every case study is a documented production finding - named, sourced, and specific. The theory you need is here, but only as much as you need to make the practical parts work. When the tools change, the DISCOVER Loop framework stays.

Written for security researchers and penetration testers who are comfortable with Python and standard tooling and want a complete, field-tested workflow for AI-assisted discovery.

Start the first campaign today. The targets are waiting.