RE//verse 2026 Training - AI Agents for Cybersecurity with Richard Johnson

RE//verse 2026 Training - AI Agents for Cybersecurity with Richard Johnson

$4,800.00 Sale

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Registration Info

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 malware analysis, reverse engineering, vulnerability research, and penetration testing. Students will learn to train, fine-tune, and apply large language models (LLMs) to solve real-world cybersecurity challenges, integrating AI-driven solutions into their daily operations. The course will provide hands-on experience with model architecture and embeddings, vector search, and 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. 

  • TRAINING:  March 2-5th, 2026
  • CONFERENCE: March 5th-7th, 2026 (requires separate purchase, begins the evening of the 5th)
  • LOCATION: Caribe Royale, Orlando, FL (discounted group rate link)
  • NOTE: Conference admission purchased separately.  Conference tickets can be purchased here.

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 to 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 must be able to read and write intermediate-level Python scripts. A foundation in reverse engineering, vulnerability research, firmware analysis, or similar is strongly recommended (see our "Which Class Is Right for You?" quiz). Students should be familiar with how the stack works, what the heap is, and some basic vulnerability classes (buffer overflow, stack smashing, etc). Guided exercises reminiscent of low-point reversing CTF challenges are integrated into the course, and students should be able to derive their own solutions.

Hardware/Software Requirements

This class will be using Python 3.10+ and LLVM/Clang on amd64 Linux. A preconfigured VMware Workstation VM will be provided as well as an amd64 Linux docker image. We will also use Google Collab notebooks for free online GPU resources. Students should have a working Google account with Google Collab access. Further instructions will be communicated prior to class. 

Class Topics

Deep Learning Fundamentals

  • Model Architectures: SVM, CNN, LSTM, Transformers
  • Tokenizers and Embeddings
  • Deep dive on Transformer models

Data Analysis and Search

  • Embeddings and Vector Search
  • Retrieval Augmented Generation (RAG) Systems
  • Malware classification and clustering

Reverse Engineering

  • LLM assisted disassembly and decompilation
  • Symbol recovery and code annotation

Code Auditing

  • Custom model evaluation benchmarks
  • Using vector search to identify patterns in code that may be vulnerable or malicious
  • Generating pattern matching signatures to hunt for code that is similar to known vulnerable code patterns (using weggli or semgrep as tools to do the pattern matching)

Fuzzing

  • Fuzzing with AFL++
  • Fuzz harness generation with LLMs
  • Crash triage and processing with LLMs

Trainers

Richard Johnson is a computer security specialist with a focus on software vulnerability analysis. As Senior Principal Security Researcher at Trellix, Richard offers over 20 years of professional expertise and leadership in the information security industry. Current responsibilities include zeroday vulnerability research and development of advanced fuzzing and automated reverse engineering solutions. Prior to Trellix, he built security research and bug hunting teams for Oracle Cloud and Cisco Talos. Richard is an internationally acclaimed training instructor who has published innovative research for over 15 years at industry leading venues including Black Hat, Defcon, Hack in the Box, RECON, and MIT.