Fine-Tuning LLMs for Cybersecurity: Mistral, Llama, AutoTrain, AutoGen, and LLM Agents

Course details

Explore the emergent field of cybersecurity enhanced by large language models (LLMs) in this detailed and interactive course. Instructor Akhil Sharma starts with the basics, including the world of open-source LLMs, their architecture and importance, and how they differ from closed-source models. Learn how to run and fine-tune models to tackle cybersecurity challenges more effectively. Gather insights for identifying new threats, generating synthetic data, performing open-source intelligence (OSINT), and scanning code vulnerabilities with hands-on examples and guided challenges. Perfect for cybersecurity professionals, IT specialists, and anyone keen on understanding how AI can bolster security protocols, this course prepares you to embrace the synergy of AI for cybersecurity, unlocking new potentials in threat detection, prevention, and response.

Learning objectives
Explain the fundamental concepts and architectures of open-source large language models (LLMs) such as GPT and transformer models.
Analyze the potential applications of LLMs in enhancing cybersecurity measures, including threat detection, penetration testing, and phishing defense.
Develop practical skills in integrating LLM-powered systems into existing cybersecurity protocols and infrastructures.
Construct and fine-tune LLMs tailored for specific cybersecurity applications, such as automated penetration testing or advanced phishing detection.
Build LLM agent workflows for security tooling such as web vulnerability scanning and OSINT.
Evaluate the effectiveness of AI-driven cybersecurity solutions and identify areas for optimization and improvement to adapt to evolving cyber threats.

Concepts

0. Introduction

01 - Introduction to LLMs and LLM agents for cybersecurity
02 - Prerequisites of the course
03 - What can be learned in this course
04 - Google Colab and other important tools
05 - How to make the most of this course

1. Open-Source LLMs and Why They're Important

06 - GenAI and top LLMs - GPT4, Claude, and Gemini (closed source)
07 - Important LLM concepts - Transformer architecture, attention, quantization, parameter offloading, and fine-tuning
08 - Open source LLMs - Llama, Mistral, Mixtral, and Code Llama
09 - Assets to find and run models - Hugging Face, Replicate, Google Colab, and Ollama
10 - Fine-tuning models - QLoRA, PEFT, and Unsloth
11 - Challenge - Import an LLM in Colab
12 - Solution - Learn how to import an LLM from Hugging Face

2. LLMs and Cybersecurity

13 - New evolving threats, powered by LLMs
14 - Advanced attacks by hackers using LLMs
15 - How cybersecurity professionals use LLMs for good
16 - Synthetic data generation - Introduction
17 - Synthetic data generation - Code example
18 - Challenge - Identify phishing emails using LLMs
19 - Solution - Fine-tune LLMs with email phishing datasets in Colab

3. Code Vulnerability Scanning with LLMs

20 - Introduction to code vulnerability scanning
21 - Blockchains and smart contract auditing
22 - Out-of-the-box output (vulnerability scanning) from an LLM
23 - Fine-tuning and mapping a dataset
24 - Training the model
25 - Inference and benchmarking

4. OSINT with LLM Agents

26 - Introduction to OSINT and how LLMs can help
27 - Introduction to agents and agent workflows
28 - Agent frameworks and Crew AI - Tools and tasks
29 - Planning the agents, their tasks, and their responsibilities
30 - Setting up the project - LLMs, Agents, and defining tasks
31 - Finishing touches and analyzing the output

5. Web Vulnerability Scanning with LLM Agents

32 - Introduction to web vulnerability scanning
33 - Planning the project and discovering the right tools
34 - Getting network and DOM data and processing it
35 - Analyzing logs and setting up tools
36 - Setting up agents and their tasks
37 - Kicking off the crew and analyzing the output

6. LLM-Powered Firewall

38 - Introduction to LLM-powered firewall projects
39 - Planning the approach
40 - Network data gathering and storing
41 - Data preprocessing
42 - LLM setup
43 - LLM fine-tuning
44 - Inference output and closing notes

7. The Future of Cybersecurity with LLMs

45 - Threats of the future
46 - LLM powered agents for hacking
47 - Decentralized botnets for decentralized DOS attacks
48 - Swarm learning with decentralized AI botnets
49 - Predictive security with LLMs
50 - Resources to take your learning further
51 - Keep up with the ever-changing dynamic tech landscape

Conclusion

52 - Summarizing the course
53 - Key learnings, best practices, and parting statements