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Learning Roadmap

How to Become a AI Red Team Engineer

A step-by-step, phase-based learning path from beginner to job-ready AI Red Team Engineer. Estimated completion: 8 months across 5 phases.

5 Phases
32 Weeks Total
High Entry Barrier
Advanced Difficulty
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  1. Foundations: AI Systems & Security Mindset

    6 weeks
    Goals
    • Understand transformer architectures, tokenization, and LLM inference pipelines
    • Learn core cybersecurity concepts: threat modeling, attack surfaces, responsible disclosure
    • Set up a local LLM development environment with Python, Hugging Face, and OpenAI API
    Resources
    • Andrej Karpathy's 'Neural Networks: Zero to Hero' lecture series
    • OWASP Top 10 for LLM Applications (2025 edition)
    • Hugging Face NLP Course (free)
    • 'The Web Application Hacker's Handbook' for security fundamentals
    Milestone

    You can fine-tune a small model, interact with LLM APIs, and articulate basic threat models for AI systems.

  2. Adversarial ML & Prompt Attack Techniques

    8 weeks
    Goals
    • Master prompt injection, jailbreaking, and indirect prompt injection techniques
    • Study adversarial examples in vision and NLP models using ART and custom scripts
    • Understand RLHF, constitutional AI, and content-filter bypass methodologies
    Resources
    • Microsoft PyRIT documentation and example notebooks
    • Academic papers: 'Universal and Transferable Adversarial Attacks on Aligned Language Models' (Zou et al.)
    • Garak LLM vulnerability scanner tutorial
    • Simon Willison's blog and 'Adversarial Machine Learning' by Goodfellow et al.
    Milestone

    You can independently discover novel prompt injection vectors and document them in a structured report.

  3. Red Team Operations & Tooling Mastery

    8 weeks
    Goals
    • Build automated red-team pipelines using PyRIT, Garak, and Promptfoo
    • Test agentic frameworks (LangChain, AutoGen) for tool-use exploitation
    • Learn structured vulnerability reporting and severity classification (CVSS-like for AI)
    Resources
    • OpenAI Red Teaming Network application guidelines and published findings
    • Anthropic's 'Core Views on AI Safety' and published red-team case studies
    • LangChain security documentation and agent threat model guides
    • MITRE ATLAS (Adversarial Threat Landscape for AI Systems)
    Milestone

    You can scope, execute, and report a full red-team engagement against a multi-turn AI application end-to-end.

  4. Specialization: Multi-Modal, Agentic & Supply-Chain Attacks

    6 weeks
    Goals
    • Analyze attack surfaces in vision-language models and audio transcription systems
    • Test autonomous agent loops for recursive exploitation and goal misalignment
    • Evaluate supply-chain risks: poisoned datasets, malicious LoRA adapters, compromised model weights
    Resources
    • NIST AI Risk Management Framework (AI RMF) and playbook
    • Research on backdoor attacks in federated learning and model merging
    • Open-source agent benchmarks (SWE-bench, AgentBench) for stress testing
    • Cloud security posture management (CSPM) for AI workloads
    Milestone

    You can design red-team exercises for cutting-edge multi-modal and agentic AI systems with confidence.

  5. Leadership: Building Red-Team Programs & Thought Leadership

    4 weeks
    Goals
    • Design an organizational AI red-team program with cadence, scope, and governance
    • Publish original research or tooling contributions to the AI safety community
    • Develop training materials and tabletop exercises for AI incident response
    Resources
    • Google DeepMind Frontier Safety Framework
    • Anthropic Responsible Scaling Policy as a governance template
    • Conference talks from DEF CON AI Village, Black Hat, and NeurIPS SafeRL workshops
    • Building an internal AI incident response playbook (synthesize from NIST, MITRE)
    Milestone

    You can lead an AI red-team function, mentor junior red-teamers, and represent your organization's AI safety posture externally.

Practice Projects

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

LLM Jailbreak Toolkit

Beginner

Build a Python library that implements 10+ classic jailbreak techniques (DAN, role-play, encoding bypasses) and tests them against OpenAI and open-source models with automated success/failure classification.

~25h
prompt_engineeringpython_programmingllm_architecture

RAG Pipeline Injection Tester

Intermediate

Create a test harness that injects adversarial content into a RAG system's knowledge base and measures whether the LLM follows injected instructions instead of the system prompt.

~30h
indirect_prompt_injectionrag_architecturered_teaming_methodology

Automated LLM Fuzzer with PyRIT

Intermediate

Build an automated fuzzing pipeline using Microsoft PyRIT that generates adversarial prompts via mutation strategies, sends them to a target model, and scores outputs for safety violations with configurable classifiers.

~35h
adversarial_mlmodel_evaluation_benchmarkingtool_orchestration

Agent Tool-Use Exploitation Lab

Advanced

Deploy a LangChain agent with simulated tool access (file system, database, API) and develop a suite of attacks that trick the agent into unauthorized actions, data exfiltration, or privilege escalation.

~40h
agentic_red_teamingexcessive_agency_testingadversarial_ml

Multi-Modal Adversarial Attack Gallery

Advanced

Research and implement adversarial image attacks (typographic attacks, adversarial patches) against open-source vision-language models, documenting which attacks transfer across models and which are model-specific.

~45h
multi_modal_attack_vectorsadversarial_mlmodel_evaluation_benchmarking

AI Red Team Report Template & Case Study

Beginner

Create a professional vulnerability report template tailored for AI systems, then populate it with a realistic case study of a simulated red-team engagement against a public LLM chatbot.

~15h
responsible_disclosure_reportingred_teaming_methodologytechnical_writing

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