Course Details
Number of Days: 4
Attendance: In-Person
Details below last updated: June 29/2026
Course Overview
This class is designed to introduce students to the most effective tools and techniques for applying cutting edge deep learning based artificial intelligence to cybersecurity tasks. By leveraging AI driven automation, students will explore new ways to enhance security workflows, improve threat detection, and optimize vulnerability research. We will take a deep dive into modern AI architectures, focusing on how deep learning models can assist in areas such as reverse engineering and vulnerability research. Students will learn to solve real world cybersecurity challenges, integrating AI driven solutions into their daily operations. The course will provide hands-on experience with advanced agent driven security automation techniques. Through practical exercises, students will gain proficiency in using AI to automate security tasks. By the end of the course, attendees will have the skills and knowledge to incorporate deep learning based AI solutions into their cybersecurity workflows, enhancing both efficiency and effectiveness.
Who Should Attend
This class is meant for professional developers or security researchers looking to add deep learning artificial intelligence based automation to cybersecurity domains. Students wanting to learn a programmatic and tool driven approach to incorporating the latest artificial intelligence capabilities into their daily work will benefit from this course.
Key Learning Objectives
Gain a fundamental understanding of how modern AI models achieve capabilities such as text completion, data classification, summarization, and analytical tasks
Understand how to leverage embeddings and vector search to give models access to proprietary or new information not available during training
Leverage deep learning for tasks related to reverse engineering and vulnerability research
Prerequisites
Students should be prepared to tackle challenging and diverse subject matter and be comfortable writing functions in Python and C to complete exercises involving Python libraries or frameworks used to build LLM enhanced tools and simple harnesses for C libraries. Attendees should also have basic experience with high level applied topics such as reverse engineering, code auditing, fuzzing, and vulnerability research.
Hardware/Software Requirements
A preconfigured Linux VM will be provided.
Class Topics
Data Analysis and Search
Embeddings and Vector Search
Retrieval Augmented Generation (RAG) Systems
LLM Agentic Tooling
Agentic CLIs
LLM tool use and function calling
Model Control Protocol
Reverse Engineering
LLM assisted disassembly and decompilation
Symbol recovery and code annotation
Fuzzing
Fuzzing with AFL++ and libFuzzer
Fuzz harness generation with LLMs
Crash triage and processing with LLMs
Automated Agentic Bug Hunting
Agent SDKs
Agentic approach to CTFs and wargames
Agentic vulnerability discovery
About the Instructor: Richard Johnson
Richard Johnson is the founder of Fuzzing IO and Metaframe AI and an internationally recognized industry leader in fuzzing, vulnerability research, reverse engineering and cybersecurity applications of deep learning. He has published dozens of research articles, presentations, and open source tools that have helped advance the field of fuzzing and reverse engineering. He is also widely known as the instructor of the premier fuzzing and agentic vulnerability research training programs available at industry cybersecurity conferences worldwide. Richard has been a professional vulnerability researcher and reverse engineer for over 20 years and has built teams from the ground up at Cisco Talos and Oracle Cloud to discover vulnerabilities at scale through fuzzing.