Agentic RE: Automating Reverse Engineering & Vulnerability Research with AI - John McIntosh - DCTLV2026

Name of Training: Agentic RE: Automating Reverse Engineering & Vulnerability Research with AI
Trainer(s): John McIntosh
Dates: August 10-11, 2026
Time: 8:00 am to 5:00 pm 
Venue: Las Vegas Convention Center
Cost: $3,750

Short Summary:

Reverse engineering is entering the Agentic Era. Turn AI into a force multiplier for real‑world reverse engineering.

In this two‑day, hands‑on progression, you’ll explore the intersection of security research, vulnerability analysis, and agentic AI—learning how to pair traditional RE skills with private LLM stacks and custom Model Context Protocol servers. You’ll leave with AI‑powered workflows that accelerate understanding, surface weaknesses, and streamline your analysis.

Course Description:

Reverse engineering is evolving beyond static tools and manual workflows. This two-day, hands-on course introduces a new paradigm: Agentic Workflows. By combining large language models (LLMs), the Model Context Protocol (MCP), and reverse engineering tools like Ghidra, participants will learn how to design, configure, and orchestrate AI-powered systems that automate and accelerate binary analysis.

Rather than focusing on a single operating system, the course demonstrates how agentic pipelines can be applied across Windows, Apple, and Android. Students will see how MCP servers and AI-assisted workflows adapt to different platforms, enabling reproducible analysis and vulnerability triage in diverse environments.

By the end of the course, participants will have built a reproducible agentic AI workflow capable of analyzing software, surfacing potential vulnerabilities, validating, and triaging results across multiple platforms.

Course Outline: 

Day 1 – Foundations & Stack Setup

• The Agentic Era: how LLMs transform reverse engineering and vulnerability research
• LLM basics: tokens, embeddings, quantization (overview only)
• Model selection & hardware trade-offs (7B, 13B, 70B models, QLoRA)
• Model Context Protocol (MCP): exposing RE tools to LLMs
• Why local LLMs matter: privacy, reproducibility, control
• Local LLM stack setup (Ollama, OpenWebUI, LM-Studio, GhidraMCP)
• Agentic-assisted reverse engineering (LLM explains and annotates code)
• Using AI to dive into new research areas

Day 2 – MCP Development & Agentic Workflow Orchestration

• MCP server basics (Python + FastAPI)
• Custom Ghidra MCP (headless scripting, function listings)
• SAST meets LLMs: leveraging context to discover vulnerabilities (Semgrep / CodeQL + LLMs)
• Learn lessons from recent DARPA AIxCC contest winners and insights from agentic bug-finding systems
• Programming with LLMs: context engineering, handling non-determinism
• Securing agentic workflows (prompt injection, sanitization)
• LLM orchestration with DSPy
• Advanced prompt optimization (MiPROv2, GEPA)
• RE HUD prototype using Streamlit/Chainlit
• Capstone: students combine deterministic RE tools with agentic reasoning to build reliable, integrated workflows for LLM‑enhanced binary analysis

If you’re a reverse‑engineering analyst aiming to speed up your understanding, an AI developer curious about applying agentic workflows to real security problems, or a vulnerability researcher exploring how AI can uncover new weaknesses, this course is built for you.

Difficulty Level:

Advanced - The student is expected to have significant practical experience with the tools and technologies that the training will focus on.

Students should be comfortable with common reverse‑engineering tools, have a basic understanding of modern AI concepts, and be able to write or modify Python scripts. You don’t need deep expertise, but familiarity will help you get the most out of the hands‑on work.

Suggested Prerequisites:

• Reverse engineering experience (familiarity with Ghidra, IDA, or similar tools)
• Basic vulnerability research knowledge (common bug classes, analysis workflows)
• Comfort with Python scripting and basic development (installing, debugging, running scripts)
• Experience using recent LLM capabilities in workflows, or at least leveraging AI to improve day-to-day tasks

What Students Should Bring:

System Requirements & Alternatives:

• A laptop with 16GB+ RAM capable of running Docker and a Linux-style command line
  
• A GPU is not required. LLM inference will be provided by the instructor during the course. Students who want to experiment with local models (e.g., Qwen3 8B via Ollama) on their own hardware can do so, but this is optional
  
• Setup instructions for both local and instructor-hosted options will be provided before the course

Software Requirements:

• Python 3.11+ 
• Docker to run OpenWebUI and Ollama (non-Docker installation also supported)
• git, linux style command-line
  

What the Trainer Will Provide:

• Preconfigured VM images and containerized stacks
• Annotated materials and scripts
• Documentation and reproducible workflows
• Experiment scaffolding
• Setup instructions for frontier models if laptop hardware can’t meet GPU requirements

Trainer(s) Bio:

John McIntosh (@clearbluejar) is a security researcher and lead instructor @clearseclabs, a company that offers hands-on training and consulting for reverse engineering and offensive security. He is the author of pyghidra-mcp, a Python-based Ghidra MCP server designed for agentic workflows and reproducible automation. He presented this work as a patch diffing agentic workflow at Objective by the Sea v8 (Reverse Engineering Apple Security Updates).

John has taught related workshops at major conferences, including:

• Supercharging Ghidra: Build Your Own Private Local LLM RE Stack with GhidraMCP, Ollama, and OpenWebUI (Ringzer0 Training)
• Offensive Security Tool Development with Ghidra: From Custom CLI Tools to an MCP Server (Recon 2025)

With over a decade of offensive security experience, John has spoken and taught at Ringzer0, 44CON, Objective by the Sea, and Recon. He regularly blogs on clearbluejar.github.io, publishing detailed write-ups on reversing CVEs and building RE tooling with Ghidra. His teaching emphasizes reproducibility, transparency, and contributor empowerment — bridging deterministic analysis with AI-driven reasoning to accelerate vulnerability research.

Registration Terms and Conditions: 

Trainings are refundable before July 11, 2026, minus a non-refundable processing fee of $250.

Between July 11, 2026 and August 5, 2026 partial refunds will be granted, equal to 50% of the course fee minus a processing fee of $250.

All trainings are non-refundable after August 5, 2026.

Training tickets may be transferred to another student. Please email us at training@defcon.org for specifics.

If a training does not reach the minimum registration requirement, it may be cancelled. In the event the training you choose is cancelled, you will be provided the option of receiving a full refund or transferring to another training (subject to availability).

Failure to attend the training without prior written notification will be considered a no-show. No refund will be given.

DEF CON Training may share student contact information, including names and emails, with the course instructor(s) to facilitate sharing of pre-work and course instructions. Instructors are required to safeguard this information and provide appropriate protection so that it is kept private. Instructors may not use student information outside the delivery of this course without the permission of the student.

By purchasing this ticket you agree to abide by the DEF CON Training Code of Conduct and the registration terms and conditions listed above.

Several breaks will be included throughout the day. Please note that food is not included.

All courses come with a certificate of completion, contingent upon attendance at all course sessions. Some courses offer an option to upgrade to a certificate of proficiency, which requires an additional purchase and sufficient performance on an end-of-course evaluation.