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Learning Roadmap

How to Become a AI Threat Hunting Specialist

A step-by-step, phase-based learning path from beginner to job-ready AI Threat Hunting Specialist. Estimated completion: 8 months across 4 phases.

4 Phases
34 Weeks Total
High Entry Barrier
Expert Difficulty
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  1. Foundational Cybersecurity & ML Theory

    8 weeks
    Goals
    • Understand core networking, security principles, and the OWASP Top 10 for LLM Applications.
    • Gain a solid grasp of supervised/unsupervised learning, neural network architectures, and training lifecycles.
    Resources
    • PortSwigger Web Security Academy
    • OWASP LLM Top 10
    • Fast.ai Practical Deep Learning Course
    • Papers: 'Adversarial Examples in the Physical World', 'Stealing Machine Learning Models via Prediction APIs'
    Milestone

    You can articulate the difference between a traditional SQL injection and a prompt injection attack, and you understand the basic components of an ML pipeline.

  2. Applied Adversarial ML & Tool Proficiency

    12 weeks
    Goals
    • Master key attack methods: adversarial examples, data poisoning, model evasion, and extraction.
    • Gain hands-on proficiency with core tools: PyTorch/TF for attacks, ART, and cloud ML platforms.
    Resources
    • CS294-129: Designing, Visualizing and Understanding Deep Neural Networks (Berkeley)
    • GitHub: CleverHans, Foolbox libraries
    • AWS/GCP/Azure ML security documentation
    • Kaggle Competitions focused on robustness
    Milestone

    You can successfully implement a basic FGSM or PGD attack on a public model, poison a small dataset, and explain the security implications of model serialization formats.

  3. Specialization in Agentic & LLM Threat Hunting

    8 weeks
    Goals
    • Deep dive into LLM-specific threats: prompt injection, jailbreaking, insecure plugin use, and data leakage.
    • Learn to set up and attack complex agent architectures using frameworks like LangChain.
    Resources
    • Trail of Bits - 'Not with a Bug, But with a Sticker' research
    • Garak LLM vulnerability scanner documentation
    • Building & breaking custom LangChain agents
    • CTF platforms with AI-focused challenges (e.g., HackTheBox)
    Milestone

    You can design and execute a multi-step attack against a RAG-based chatbot to exfiltrate its context or bypass safety filters.

  4. Operationalization & Threat Intelligence

    6 weeks
    Goals
    • Learn to build detection and monitoring for AI threats in production.
    • Develop skills in threat intelligence reporting and creating actionable hunt hypotheses.
    Resources
    • MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
    • Papers on ML model monitoring
    • Practice building dashboards in Kibana/Grafana for ML metrics
    • Template for red team engagement reports
    Milestone

    You can draft a comprehensive threat hunt hypothesis, build a preliminary detection rule, and write a technical summary of your findings for both engineers and management.

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

LLM Red Team Toolkit

Advanced

Build a Python framework that automates testing of LLM APIs against common attack vectors (prompt injection, jailbreaking, data leakage). It should include a library of attack prompts, a response classifier, and generate a security report.

~40h
Python ScriptingLLM API IntegrationAdversarial Prompting

Model Poisoning Simulation Lab

Intermediate

Create a safe, isolated environment (using Docker) to simulate data poisoning attacks on a simple image classifier. Implement and visualize different poisoning strategies (backdoor, clean-label) and test detection methods.

~30h
DockerData Poisoning TechniquesModel Monitoring

AI Threat Intelligence Feed Parser

Beginner

Write scripts to aggregate and parse public AI vulnerability disclosures (from GitHub advisories, MITRE ATLAS, research blogs) into a structured, searchable threat intelligence database.

~15h
Web Scraping/Data ParsingThreat Intelligence FundamentalsDatabase Basics

Adversarial Example Generator & Visualizer

Intermediate

Develop a web app that allows a user to upload an image, select a model, and use ART or custom code to generate adversarial examples with different attacks (FGSM, PGD), displaying the perturbation and model confidence changes.

~25h
Adversarial Machine LearningWeb Development BasicsData Visualization

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