Metaframe AI>

AI Agents for Cybersecurity

Instructor: Richard Johnson, Metaframe AI

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 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.

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

Prerequisite Knowledge

Students should be prepared to tackle challenging and diverse subject matter and be comfortable writing functions in python and C to complete exercises involving using python libraries or frameworks to write LLM enhanced tools and simple harnesses for C libraries. Attendees should also have basic experience with the high level applied topics such as reverse engineering, code auditing, fuzzing, and web penetration testing.

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 Analysis

• Machine learning algorithms
• EMBER malware analysis dataset
• Malware classification and clustering
• LSTM based anomaly detection

LLM Agentic Tooling

• Agentic CLIs
• LLM tool use and functoi calling
• Model Control Protocol

Reverse Engineering

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

Fuzzing

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

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)

Automated Agentic Bug Hunting

• Agent SDKs
• Agentic approach to CTFs and wargames
• Agentic vulnerability discovery

Last Updated: December 2025

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