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The Free Self-Directed AI Security Mastery Path - Brought to you by Cameron Hopkin Founder of Waypoint Compliance Advisory

Zero-Cost Route to Elite AI Security Expertise

License: MIT Contributions Welcome Last Updated

Platform: Optimized for Kubuntu/Ubuntu 24.04 LTS with Python virtual environments

Why This Exists

The AI security training market charges $5,000-$15,000 for courses teaching skills you can learn for free. This repository is a complete, structured learning path built from publicly available resources, open-source tools, and hands-on labs.

This path is designed for:

  • Security professionals expanding into AI/ML security
  • Penetration testers wanting to add LLM red teaming skills
  • Blue teamers building AI defense capabilities
  • Anyone with security fundamentals ready to specialize

What you'll learn:

  • Prompt injection, jailbreaking, and LLM exploitation
  • Adversarial machine learning attacks and defenses
  • RAG poisoning and agent security
  • Deepfake detection and synthetic media forensics
  • AI security governance and compliance frameworks

Time commitment: 2-3 hours daily for 36 weeks (or accelerate based on your background)

Where This Sits

This lab is the on-ramp: self-directed, hands-on, and free. If you want the same subject matter treated at research level, the applied AI security and assurance curriculum is published in full, including the complete LLM and agent red teaming syllabus. It is a body of knowledge and a reference, not a program you enroll in, and it is free to read and free to borrow from.

Table of Contents

Environment Setup

🚀 Quick Start (Kubuntu/Ubuntu 24.04)

Ubuntu 24.04+ enforces PEP 668 to protect system Python. We use virtual environments (the professional approach).

Option 1: Automated Setup (Recommended)

# Clone this repository
git clone https://github.com/WaypointCA/ai-security-lab.git
cd ai-security-lab

# Run the complete setup script
bash scripts/setup_ai_security_lab.sh

Option 2: Quick Manual Setup

# Install Python venv support
sudo apt install python3-full python3-venv python3-pip

# Create and activate virtual environment
cd ~/ai-security-lab
python3 -m venv venv
source venv/bin/activate

# Install packages
pip install --upgrade pip
pip install -r requirements.txt

Option 3: Minimal 2-Minute Start

bash scripts/quick_start.sh

📁 Repository Structure

ai-security-lab/
├── README.md                      # This file
├── LICENSE                        # MIT License
├── CONTRIBUTING.md                # Contribution guidelines
├── requirements.txt               # Full Python dependencies
├── requirements-minimal.txt       # Minimal dependencies to start
├── scripts/
│   ├── setup_ai_security_lab.sh  # Complete setup automation
│   ├── quick_start.sh            # 2-minute quick start
│   ├── activate.sh               # Daily environment activation
│   └── test_setup.py             # Verify installation
├── phases/
│   ├── phase1-foundation/
│   ├── phase2-llm-security/
│   ├── phase3-adversarial-ml/
│   ├── phase4-lab-setup/
│   ├── phase5-deepfakes/
│   ├── phase6-purple-team/
│   ├── phase7-real-world/
│   └── phase8-advanced/
├── tools/                         # Cloned security tools
├── labs/                          # Lab exercises by week
├── projects/                      # Your security projects
└── venv/                          # Python virtual environment (auto-created)

🔧 Daily Workflow

Start Your Day

# Using the alias (if you set it up)
ai-lab

# Or manually
cd ~/ai-security-lab
source venv/bin/activate

Verify Environment

python scripts/test_setup.py

Update Tools

pip install --upgrade -r requirements.txt

💡 Pro Setup Tips

Add Convenient Alias

echo "alias ai-lab='cd ~/ai-security-lab && source venv/bin/activate'" >> ~/.bashrc
source ~/.bashrc
# Now just type: ai-lab

VS Code Integration

  1. Open folder in VS Code: code ~/ai-security-lab
  2. Ctrl+Shift+P → "Python: Select Interpreter"
  3. Choose ./venv/bin/python
  4. VS Code auto-activates venv in terminals

Jupyter Setup

# After activating venv
pip install jupyter ipykernel
python -m ipykernel install --user --name=ai-security
# Now select "ai-security" kernel in Jupyter

Phase 1: Foundation (Weeks 1-4)

Learn Just Enough ML to Break It

Week 1-2: ML Fundamentals for Attackers

Free Learning Resources:

Hands-On Labs (Free):

Required Reading:

Week 1 Lab Exercises:

# In your activated environment
cd ~/ai-security-lab/labs/week01
jupyter lab week01_ml_basics.ipynb
  • Train a basic image classifier on CIFAR-10
  • Generate your first adversarial example using FGSM
  • Document the attack in a blog post

Week 3-4: AI Security Frameworks

Study Materials (All Free):

YouTube Channels to Follow:

Practical Exercises:

  • Map 5 recent AI breaches to MITRE ATLAS tactics
  • Create your own threat model for a hypothetical AI system
  • Join the OWASP Slack (#project-top10-for-llm channel)

Phase 2: Offensive LLM Security (Weeks 5-12)

Master Prompt Injection and Jailbreaking

Week 5-6: Prompt Injection Mastery

Free Resources:

Install and Master These Tools:

# In your activated venv
pip install garak promptfoo
git clone https://github.com/Azure/PyRIT.git tools/PyRIT
cd tools/PyRIT && pip install -e .

Tools Documentation:

Practice Targets (All Free):

Week 5-6 Lab Setup:

# Install Ollama for local testing
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama2
ollama pull mistral

# Test with garak
garak --model_type ollama --model_name llama2

Week 7-8: Advanced Jailbreaking

Research Papers (Free on arXiv):

Free Labs:

cd ~/ai-security-lab/labs/week07
python jailbreak_automation.py
  1. Set up Ollama with multiple models
  2. Test jailbreaks across models for transferability
  3. Build automated jailbreak generator using PyRIT
  4. Document 10 working jailbreaks with success rates

Week 9-10: Model Extraction and Privacy Attacks

Learning Resources:

Free API Practice:

Week 11-12: RAG and Agent Security

Build Your Lab:

# In activated venv
cd ~/ai-security-lab

# Pull models for RAG testing
ollama pull llama2
ollama pull mistral
ollama pull mixtral

# Install AnythingLLM
docker pull mintplexlabs/anythingllm
docker run -d -p 3001:3001 --name anythingllm mintplexlabs/anythingllm

Free Learning Path:

Projects to Build:

  • Poison a RAG system's knowledge base
  • Exploit agent tool-calling with malicious prompts
  • Chain attacks across multiple agents
  • Write detailed attack methodology

Phase 3: Classical Adversarial ML (Weeks 13-16)

Beyond LLMs - Computer Vision and Traditional ML

Week 13-14: Adversarial Examples

Install Frameworks in venv:

pip install adversarial-robustness-toolbox foolbox cleverhans textattack

Free Frameworks:

Learning Resources:

Build These Attacks:

# Week 13 lab template
from art.attacks.evasion import FastGradientMethod
from art.estimators.classification import PyTorchClassifier
# Your code here
  • FGSM, PGD, C&W against ImageNet models
  • Physical adversarial patches (printable)
  • Black-box attacks with limited queries
  • Transfer attacks between models

Week 15-16: Data Poisoning

Critical Research (Free):

Hands-On Experiments:

  • Poison MNIST classifier with 1% bad data
  • Implement backdoor trigger in neural network
  • Clean-label poisoning attack
  • Test defenses and bypass them

Phase 4: Building Your Security Lab (Weeks 17-20)

Professional Testing Environment at Zero Cost

Week 17-18: Local AI Security Lab

Complete Lab Stack Setup:

# After environment activation
cd ~/ai-security-lab

# Install full requirements
pip install -r requirements-full.txt

Vulnerable Apps to Deploy:

cd ~/ai-security-lab/tools
git clone https://github.com/dhammon/ai-goat.git
git clone https://github.com/DamnVulnerableLLM/DamnVulnerableLLM.git
git clone https://github.com/orcasecurity-research/AIGoat.git
git clone https://github.com/AImaginationLab/vulnerable-llms.git

Vulnerable App Links:

Week 19-20: Automation and Tooling

Build Your Own Tools:

# Template structure
cd ~/ai-security-lab/projects
mkdir prompt-injection-scanner
cd prompt-injection-scanner
touch scanner.py requirements.txt README.md
  • Prompt injection scanner (Python + Garak)
  • Automated jailbreak tester
  • Model extraction framework
  • RAG poisoning toolkit

Free Infrastructure:

Phase 5: Deepfakes and Synthetic Media (Weeks 21-22)

Understanding the $25M Threat Vector

Week 21: Generation Techniques

Free Tools:

Detection Tools (Free):

Week 22: Defense and Detection

Build These Projects:

  • Deepfake detection model using ResNet
  • Audio deepfake classifier
  • Metadata forensics toolkit
  • Corporate defense playbook

Phase 6: Purple Team Integration (Weeks 23-26)

Bridging Offense and Defense

Week 23-24: Blue Team Defenses

Free Defensive Tools:

SIEM Integration (Free):

Week 25-26: Compliance and Governance

Free Compliance Resources:

Build Compliance Artifacts:

  • AI Risk Register template
  • Assessment questionnaires
  • Governance framework document
  • Vendor assessment checklist

Phase 7: Real-World Practice (Weeks 27-30)

Building Your Portfolio

Week 27-28: Bug Bounties and CTFs

Free Practice Platforms:

Strategy for Success:

  1. Start with programs explicitly mentioning AI/LLM
  2. Focus on prompt injection and data leakage initially
  3. Document everything for portfolio
  4. Aim for 5-10 valid submissions

Week 29-30: Open Source Contributions

High-Value Contribution Targets:

Portfolio Projects:

  • Security assessment of popular open-source AI project
  • White paper on emerging AI threat
  • Tool release on GitHub
  • Blog series (10+ technical posts)

Phase 8: Advanced Specialization (Weeks 31-36)

Becoming the Expert

Week 31-32: Nation-State TTPs

Intelligence Sources (Free):

Analysis Framework:

  • Track 5 APT groups using AI
  • Map their TTPs to MITRE ATLAS
  • Build detection strategies
  • Publish threat brief

Week 33-34: Cutting-Edge Research

Stay Current With:

Twitter/X Accounts to Follow:

  • Nicholas Carlini (@nicholas_carlini)
  • Simon Willison (@simonw)
  • Kai Greshake (@KGreshake)
  • Rich Harang (@rharang)
  • Will Pearce (@moo_hax)

Week 35-36: Capstone Project Options

Option 1: Security Tool Development

  • Build comprehensive AI security scanner
  • 1000+ stars on GitHub as goal
  • Full documentation and examples
  • Conference talk submission

Option 2: Major Vulnerability Research

  • Find significant vulnerability in popular AI system
  • Responsible disclosure
  • Detailed write-up
  • Conference presentation

Option 3: Defensive Framework

  • Complete security framework for specific industry
  • Open-source release
  • Implementation guides
  • Community adoption

Essential Resources

Documentation and Frameworks

Learning Platforms (Free)

Community and Forums

Free Compute Resources

Progress Tracking

6-Month Milestones

Technical Skills

  • Execute 10 different attack types
  • Build 5 security tools
  • Find 10 valid vulnerabilities
  • Complete 20 CTF challenges
  • Contribute to 3 major projects

Portfolio Development

  • 20+ technical blog posts
  • 1000+ GitHub commits
  • 5+ detailed vulnerability write-ups
  • 3+ conference talk proposals
  • 10+ tool demonstrations

Professional Growth

  • 500+ LinkedIn connections in AI security
  • 3+ consulting inquiries received
  • 1+ paid engagement completed
  • Known expert in 3+ communities

Weekly Time Commitment

Daily (2-3 hours)

  • Morning (30 min): Read one research paper, review security news
  • Lunch (30 min): Watch one conference talk, practice one CTF challenge
  • Evening (1-2 hours): Hands-on lab work, tool development, blog writing

Weekend (4-6 hours):

  • Deep dive into new attack technique
  • Build/improve security tool
  • Write comprehensive blog post
  • Participate in CTF or bug bounty

Requirements Files

requirements.txt (Core Dependencies)

# Essential AI Security Tools
garak
promptfoo
adversarial-robustness-toolbox
foolbox
textattack

# Core ML Libraries
torch
transformers
tensorflow
numpy
pandas
scikit-learn

# LLM Tools
langchain
openai
anthropic

# Development
jupyter
streamlit
gradio

requirements-minimal.txt (Quick Start)

numpy
pandas
requests
jupyter
notebook

See requirements-full.txt for complete dependencies including all frameworks and tools.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Ways to contribute:

  • Add new resources you discover
  • Update links that have changed
  • Share lab solutions (ethically)
  • Contribute tools back to the community
  • Report issues or suggest improvements

License

This project is licensed under the MIT License - see the LICENSE file for details.

About

This learning path was compiled and is maintained by Waypoint Compliance Advisory LLC, a Service-Disabled Veteran-Owned Small Business (SDVOSB) specializing in cybersecurity consulting, CMMC compliance, and AI security assessments.

Contact: info@waypointcompliance.com

The knowledge is free. The tools are free. The communities are free. Your only investment is time and determination.

Last Updated: January 2026

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Free 36-week self-directed AI security learning path

waypointca.com

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