Skip to main content

Learning Roadmap

How to Become a AI Red Team Specialist

A step-by-step, phase-based learning path from beginner to job-ready AI Red Team Specialist. Estimated completion: 7 months across 5 phases.

5 Phases
30 Weeks Total
High Entry Barrier
Expert Difficulty
← AI Red Team Specialist Overview Interview Prep →
Your Progress 0 / 5 phases

Progress saved in your browser — no account needed.

  1. Foundations: ML, Security, and LLM Internals

    6 weeks
    Goals
    • Understand transformer architecture, tokenization, attention mechanisms, and alignment techniques
    • Learn core cybersecurity concepts: threat modeling, attack surfaces, vulnerability classification
    • Set up a local LLM lab environment with open-weight models (Llama, Mistral) for safe experimentation
    • Build fluency in Python for API interaction, scripting, and basic automation
    Resources
    • Stanford CS324 - LLMs course materials
    • OWASP Top 10 for LLM Applications (2025 edition)
    • HuggingFace NLP course (free)
    • TryHackMe / HackTheBox intro modules for security fundamentals
    • Karpathy's 'Let's build GPT from scratch' video
    Milestone

    You can explain how an LLM generates text, articulate the OWASP LLM Top 10, and run a local model for testing.

  2. Prompt Injection & Jailbreak Mastery

    6 weeks
    Goals
    • Master direct and indirect prompt injection techniques against multiple LLM providers
    • Learn jailbreak taxonomy: DAN-style, role-play, encoding bypasses, multi-language exploits
    • Understand system prompt extraction, context window manipulation, and output filtering bypasses
    • Practice chaining vulnerabilities (e.g., prompt injection → data exfiltration via RAG)
    Resources
    • OWASP LLM vulnerability test cases repository
    • Garak documentation and example attack plugins
    • Anthropic's research on jailbreaking and constitutional AI
    • Microsoft PyRIT tutorial and red team notebooks
    • Simon Willison's blog on LLM security incidents
    Milestone

    You can independently discover and document prompt injection vulnerabilities in a target LLM application using both manual and semi-automated techniques.

  3. Adversarial ML & Automated Testing

    8 weeks
    Goals
    • Study adversarial robustness literature: FGSM, PGD, model extraction, membership inference
    • Build automated red teaming pipelines using Garak, PyRIT, and custom Promptfoo configurations
    • Learn to evaluate model outputs at scale with LLM-as-judge and statistical analysis
    • Explore training data poisoning attack and detection techniques
    Resources
    • IBM Adversarial Robustness Toolbox (ART) documentation
    • Goodfellow et al., 'Explaining and Harnessing Adversarial Examples'
    • NIST AI Risk Management Framework (AI RMF 1.0)
    • TensorTrust challenge for hands-on prompt injection practice
    • MITRE ATLAS knowledge base for adversarial ML
    Milestone

    You can build a reproducible automated red team pipeline that tests an LLM application against 50+ attack vectors and generates structured results.

  4. Advanced Attack Surfaces & Multi-Modal Red Teaming

    6 weeks
    Goals
    • Develop expertise in multi-modal attack vectors targeting vision-language and code-generation models
    • Learn RAG-specific attacks: retrieval poisoning, context injection, source manipulation
    • Study AI agent/tool-use security: function-calling exploits, plugin abuse, autonomous agent misalignment
    • Practice supply-chain attacks on AI systems (malicious models, backdoored LoRA adapters, compromised datasets)
    Resources
    • OWASP Top 10 for LLM Applications - RAG and agent extensions
    • Research papers on adversarial attacks against vision-language models (CLIP, GPT-4V)
    • Microsoft's 'Lessons from red-teaming 100+ generative AI products'
    • DEF CON AI Village CTF challenges and write-ups
    • Anthropic's research on mechanistic interpretability and Sleeper Agents
    Milestone

    You can design and execute a comprehensive multi-modal red team engagement covering text, image, code, and agent-based attack surfaces.

  5. Professional Practice & Career Launch

    4 weeks
    Goals
    • Master professional red team report writing with CVSS-style severity scoring for AI vulnerabilities
    • Build a portfolio of 3-5 published attack case studies or responsible disclosure reports
    • Develop communication skills for presenting technical AI risks to non-technical executives
    • Engage with the AI security community through conferences (DEF CON AI Village, Black Hat, NeurIPS SafeAI) and open-source contributions
    Resources
    • Template red team report frameworks from CISA and OWASP
    • Responsible disclosure guidelines (Google, Microsoft, OpenAI programs)
    • Bug bounty platforms (HackerOne, Bugcrowd) with AI/ML scopes
    • AI security community: AI Village Discord, OWASP AI Exchange Slack
    Milestone

    You can conduct a full-scope AI red team engagement independently, produce a professional report, and present findings to stakeholders.

Practice Projects

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

Jailbreak Discovery Lab

Beginner

Set up a local LLM environment and systematically attempt jailbreak techniques (role-play, encoding, multi-language, chain-of-thought manipulation) against open-weight models. Document every bypass you find with reproducible prompts, categorize by technique family, and build a personal jailbreak taxonomy.

~25h
prompt_engineeringllm_architecturellm_attack_techniques

Prompt Injection Attack Framework

Intermediate

Build a Python-based command-line tool that automates prompt injection testing against LLM API endpoints. Support template-based attack generation, concurrent testing, output classification (success/failure), and JSON report generation. Integrate with at least two LLM providers.

~40h
python_programmingllm_attack_techniquesautomated_red_teaming

OWASP LLM Top 10 Compliance Scanner

Intermediate

Create an automated scanner that tests an LLM application against each of the OWASP LLM Top 10 vulnerability categories, generating a compliance report with severity scores, evidence screenshots, and remediation recommendations. Use Garak or Promptfoo as the underlying engine.

~35h
owasp_llm_top10red_team_methodologyreporting_and_communication

RAG Poisoning Proof-of-Concept

Intermediate

Build a vulnerable RAG application, then demonstrate how an attacker can poison the knowledge base to manipulate the system's responses. Document the attack chain from document insertion to output manipulation, and implement a detection mechanism for poisoned retrievals.

~30h
llm_attack_techniquesai_threat_modelingpython_programming

Multi-Turn Attack Simulator

Advanced

Design and implement a system that simulates multi-turn adversarial conversations against an LLM chatbot, using a planner agent that adaptively adjusts its attack strategy based on the target's responses. Evaluate which multi-turn patterns are most effective at bypassing safety measures.

~50h
automated_red_teamingllm_attack_techniquesadversarial_ml_theory

Adversarial Robustness Benchmark

Advanced

Create a benchmark suite that evaluates LLM robustness across 100+ attack vectors spanning prompt injection, jailbreaking, data extraction, and output manipulation. Test at least three different models and publish a comparative robustness report with methodology transparency.

~60h
adversarial_ml_theoryautomated_red_teamingreporting_and_communication

AI Agent Security Auditor

Advanced

Build a tool that audits an AI agent's tool-use permissions and tests for excessive agency by attempting unauthorized tool calls, parameter injection, and multi-step privilege escalation. Generate a risk report with specific attack scenarios and recommended guardrails.

~45h
ai_threat_modelingsecure_ai_architecturellm_attack_techniques

Red Team Engagement Simulation End-to-End

Advanced

Conduct a full-scope red team engagement against a purpose-built AI application (e.g., customer support chatbot with RAG and tool access). Deliver a professional report covering scope, methodology, findings with CVSS scores, evidence, and prioritized remediation guidance. Present findings to a mock stakeholder panel.

~80h
red_team_methodologyreporting_and_communicationai_threat_modeling

Ready to Start Your Journey?

Prep for interviews alongside your learning — it reinforces every concept.

Practice Interview Questions Explore More Careers