The Free Self-Directed AI Security Mastery Path

Zero-Cost Route to Elite AI Security Expertise

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

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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)

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Table of Contents

• Environment Setup
• Phase 1: Foundation (Weeks 1-4)
• Phase 2: Offensive LLM Security (Weeks 5-12)
• Phase 3: Classical Adversarial ML (Weeks 13-16)
• Phase 4: Building Your Security Lab (Weeks 17-20)
• Phase 5: Deepfakes and Synthetic Media (Weeks 21-22)
• Phase 6: Purple Team Integration (Weeks 23-26)
• Phase 7: Real-World Practice (Weeks 27-30)
• Phase 8: Advanced Specialization (Weeks 31-36)
• Essential Resources
• Progress Tracking
• Contributing

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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

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Phase 1: Foundation (Weeks 1-4)

Learn Just Enough ML to Break It

Week 1-2: ML Fundamentals for Attackers

Free Learning Resources:

• Fast.ai Course (Part 1): https://course.fast.ai - Just lessons 1-3 for basics
• Andrew Ng's ML Course: Coursera ML Course (audit free) - Only weeks 1-3 needed
• 3Blue1Brown Neural Network Series: YouTube Playlist
• Google's Machine Learning Crash Course: https://developers.google.com/machine-learning/crash-course

Hands-On Labs (Free):

• Google Colab: https://colab.research.google.com (free GPU/TPU access)
• Kaggle Notebooks: https://www.kaggle.com/code (free compute)
• Local Jupyter: Run jupyter lab in your activated venv

Required Reading:

• Adversarial Examples: Attacks and Defenses for Deep Learning
• The Limitations of Deep Learning in Adversarial Settings
• NIST AI 100-2e2025 - Adversarial Machine Learning

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):

• MITRE ATLAS: https://atlas.mitre.org - Complete framework study
• OWASP Top 10 for LLM Applications: https://owasp.org/www-project-top-10-for-large-language-model-applications/
• NIST AI RMF: https://www.nist.gov/itl/ai-risk-management-framework
• AI Incident Database: https://incidentdatabase.ai - Study real breaches

YouTube Channels to Follow:

• Two Minute Papers
• Yannic Kilcher
• DEF CON Conference
• Black Hat

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)

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

Master Prompt Injection and Jailbreaking

Week 5-6: Prompt Injection Mastery

Free Resources:

• Simon Willison's Blog Series: https://simonwillison.net/series/prompt-injection/
• Prompt Injection Handbook: https://github.com/utkusen/prompt-injection-handbook
• LangChain Security Documentation: https://python.langchain.com/docs/security
• PortSwigger Web Security Academy: https://portswigger.net/web-security/llm-attacks
• Lakera Blog on Prompt Injection: https://www.lakera.ai/blog/guide-to-prompt-injection

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:

• Garak (NVIDIA): https://github.com/NVIDIA/garak
• Promptfoo: https://github.com/promptfoo/promptfoo
• PyRIT (Microsoft): https://github.com/Azure/PyRIT

Practice Targets (All Free):

• Gandalf CTF: https://gandalf.lakera.ai (levels 1-8)
• HackAPrompt: https://www.aicrowd.com/challenges/hackaprompt-2023
• AI Village CTF Challenges: https://github.com/aivillage
• Local Ollama models: https://ollama.ai

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):

• Universal and Transferable Adversarial Attacks on Aligned Language Models
• Jailbroken: How Does LLM Safety Training Fail?
• Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
• Red Teaming the Mind of the Machine

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:

• Extracting Training Data from Large Language Models
• Model Extraction of BERT-based APIs
• Nicholas Carlini's Blog: https://nicholas.carlini.com
• I Know What You Trained Last Summer - Model Extraction Survey

Free API Practice:

• OpenAI API free tier: https://platform.openai.com/signup
• Anthropic Claude API: https://www.anthropic.com/api
• Google AI Studio: https://aistudio.google.com

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:

• LangChain Security Docs: https://python.langchain.com/docs/security
• LlamaIndex Security: https://docs.llamaindex.ai/en/stable/module_guides/security/
• OWASP LLM AI Security Cheat Sheet: https://github.com/OWASP/CheatSheetSeries/blob/master/cheatsheets/LLM_AI_Security_Cheat_Sheet.md

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

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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:

• ART (IBM): https://github.com/Trusted-AI/adversarial-robustness-toolbox
• Foolbox: https://github.com/bethgelab/foolbox
• CleverHans: https://github.com/cleverhans-lab/cleverhans
• TextAttack: https://github.com/QData/TextAttack

Learning Resources:

• Adversarial ML Tutorial (Ian Goodfellow)
• Explaining and Harnessing Adversarial Examples
• Awesome ML for Cybersecurity: https://github.com/jivoi/awesome-ml-for-cybersecurity

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):

• Poisoning Language Models During Instruction Tuning
• Backdoor Attacks on Deep Learning Models
• Sleeper Agents (Anthropic)
• Model Namespace Reuse Attack

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

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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:

• AI-Goat: https://github.com/dhammon/ai-goat
• Damn Vulnerable LLM: https://github.com/DamnVulnerableLLM/DamnVulnerableLLM
• AIGoat (Orca): https://github.com/orcasecurity-research/AIGoat
• Vulnerable LLMs: https://github.com/AImaginationLab/vulnerable-llms

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:

• GitHub Actions: https://github.com/features/actions (2000 minutes/month free)
• Cloudflare Workers: https://workers.cloudflare.com (100k requests/day free)
• Fly.io: https://fly.io (free tier for hosting)
• ngrok: https://ngrok.com (exposing local services)
• GitHub Codespaces: https://github.com/features/codespaces (120 hrs/month)

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

Understanding the $25M Threat Vector

Week 21: Generation Techniques

Free Tools:

• Stable Diffusion WebUI: https://github.com/AUTOMATIC1111/stable-diffusion-webui
• RVC for Voice: https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI
• DeepFaceLab: https://github.com/iperov/DeepFaceLab
• Wav2Lip: https://github.com/Rudrabha/Wav2Lip

Detection Tools (Free):

• Deepware Scanner: https://scanner.deepware.ai
• Sensity AI: https://sensity.ai/deepfakes-detection/
• Intel FakeCatcher: https://www.intel.com/content/www/us/en/research/responsible-ai-fakecatcher.html

Week 22: Defense and Detection

Build These Projects:

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

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

Bridging Offense and Defense

Week 23-24: Blue Team Defenses

Free Defensive Tools:

• NeMo Guardrails (NVIDIA): https://github.com/NVIDIA/NeMo-Guardrails
• Lakera Guard: https://www.lakera.ai/lakera-guard (API with free tier)
• LangKit: https://github.com/whylabs/langkit
• Evidently AI: https://github.com/evidentlyai/evidently

SIEM Integration (Free):

• Elastic Stack: https://www.elastic.co/elastic-stack
• Grafana + Loki: https://grafana.com/oss/loki/
• OpenSearch: https://opensearch.org

Week 25-26: Compliance and Governance

Free Compliance Resources:

• ISO/IEC 23053:2022 (Framework for AI trustworthiness)
• EU AI Act Full Text
• NIST AI RMF Playbook
• ISACA AI Governance (free with registration)

Build Compliance Artifacts:

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

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

Building Your Portfolio

Week 27-28: Bug Bounties and CTFs

Free Practice Platforms:

• HackerOne: https://www.hackerone.com/ai-security
• Bugcrowd: https://www.bugcrowd.com
• Intigriti: https://www.intigriti.com
• CTFtime.org: https://ctftime.org
• PentesterLab: https://pentesterlab.com/exercises (some free exercises)

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:

• OWASP Top 10 for LLMs: https://github.com/OWASP/www-project-top-10-for-large-language-model-applications
• MITRE ATLAS: https://github.com/mitre-atlas/atlas
• Garak: https://github.com/NVIDIA/garak
• PyRIT: https://github.com/Azure/PyRIT
• AI Village: https://github.com/aivillage

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)

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Phase 8: Advanced Specialization (Weeks 31-36)

Becoming the Expert

Week 31-32: Nation-State TTPs

Intelligence Sources (Free):

• Microsoft Security Blog
• Google Threat Analysis Group
• OpenAI Security
• Mandiant Intelligence
• CrowdStrike Intelligence

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:

• arXiv AI Security: https://arxiv.org/list/cs.CR/recent (check daily)
• Papers With Code - Adversarial: https://paperswithcode.com/task/adversarial-attack
• AI Safety Papers: https://www.alignmentforum.org
• Conference Videos: Media.ccc.de, InfoCon.org

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

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Essential Resources

Documentation and Frameworks

• OWASP AI Security: https://owaspai.org
• MITRE ATLAS Navigator: https://mitre-atlas.github.io/atlas-navigator/
• NIST AI Publications: https://www.nist.gov/artificial-intelligence
• AI Incident Database: https://incidentdatabase.ai
• Awesome AI Security Lists:
  • https://github.com/ottosulin/awesome-ai-security
  • https://github.com/TalEliyahu/Awesome-AI-Security

Learning Platforms (Free)

• fast.ai: https://www.fast.ai
• FreeCodeCamp: https://www.freecodecamp.org
• Coursera (Audit Mode): https://www.coursera.org
• MIT OpenCourseWare: https://ocw.mit.edu
• Kaggle Learn: https://www.kaggle.com/learn

Community and Forums

• AI Village Discord: https://aivillage.org/discord
• OWASP Slack: https://owasp.slack.com
• Reddit Communities:
  • r/netsec: https://reddit.com/r/netsec
  • r/MachineLearning: https://reddit.com/r/MachineLearning
  • r/LocalLLaMA: https://reddit.com/r/LocalLLaMA
• Hugging Face Forums: https://discuss.huggingface.co

Free Compute Resources

• Google Colab: https://colab.research.google.com
• Kaggle Notebooks: https://www.kaggle.com/code
• GitHub Codespaces: https://github.com/features/codespaces (120 hrs/month)
• Gitpod: https://www.gitpod.io (50 hrs/month)
• Paperspace Gradient: https://gradient.run (free tier)

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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

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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.

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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

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License

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

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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

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

Last Updated: January 2026