Skip to main content

Learning Roadmap

How to Become a AI Security Code Review Specialist

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

5 Phases
30 Weeks Total
High Entry Barrier
Advanced Difficulty
← AI Security Code Review Specialist Overview Interview Prep →
Your Progress 0 / 5 phases

Progress saved in your browser — no account needed.

  1. Foundations - Application Security & Secure Code Review

    6 weeks
    Goals
    • Master OWASP Top 10 for web applications and understand SAST/DAST tooling
    • Gain fluency in reading Python and TypeScript codebases with a security lens
    • Set up and operate Semgrep, Bandit, and GitLeaks for automated code scanning
    Resources
    • OWASP Code Review Guide v2
    • PortSwigger Web Security Academy (free)
    • Semgrep documentation and rule-writing tutorials
    • Book: 'Secure by Design' by Dan Bergh Johnsson
    Milestone

    You can perform a thorough security code review on a standard web application and produce actionable findings with remediation guidance.

  2. AI/ML Systems Literacy

    6 weeks
    Goals
    • Understand transformer architecture, tokenization, embeddings, and model serving at a conceptual and code level
    • Build a basic RAG pipeline using LangChain or LlamaIndex to internalize the architecture
    • Learn how models are fine-tuned, serialized, distributed, and loaded in production environments
    Resources
    • Fast.ai Practical Deep Learning course (selected modules)
    • LangChain documentation and quickstart tutorials
    • HuggingFace Transformers library documentation
    • AWS SageMaker or GCP Vertex AI deployment tutorials
    Milestone

    You can read and reason about any AI application codebase - from data ingestion through model inference to response delivery - and identify its core components and data flows.

  3. AI-Specific Security Threats & Attack Surfaces

    6 weeks
    Goals
    • Master the OWASP Top 10 for LLM Applications and MITRE ATLAS matrix
    • Learn prompt injection taxonomy - direct, indirect, multi-turn, tool-use escalation
    • Understand model extraction, inversion, membership inference, and data poisoning attacks at a practical level
    Resources
    • OWASP Top 10 for LLM Applications (2025 edition)
    • MITRE ATLAS knowledge base and case studies
    • Anthropic's research on prompt injection and jailbreaks
    • Simon Willison's blog on LLM security patterns
    • NIST AI Risk Management Framework (AI RMF 1.0)
    Milestone

    You can threat-model any AI system, identify the top 5 attack vectors specific to its architecture, and articulate exploitability and impact to stakeholders.

  4. Tool Mastery - AI Red Teaming & Automated Scanning

    6 weeks
    Goals
    • Operate Garak and PyRIT for automated LLM vulnerability scanning
    • Write custom Semgrep and CodeQL rules for AI-specific vulnerability patterns (unsafe pickle, prompt template injection, unfiltered LLM output)
    • Integrate AI security checks into CI/CD pipelines using GitHub Actions or GitLab CI
    Resources
    • Garak LLM vulnerability scanner documentation and GitHub repo
    • Microsoft PyRIT (Python Risk Identification Toolkit) docs
    • Semgrep rule-writing workshop (returntocorp tutorials)
    • GitHub Advanced Security documentation
    Milestone

    You can build and operate a comprehensive AI security scanning pipeline that catches prompt injection, insecure model loading, and excessive tool permissions before code reaches production.

  5. Production Practice - Portfolio & Industry Readiness

    6 weeks
    Goals
    • Complete 3-5 open-source AI security audit case studies with published write-ups
    • Contribute custom Semgrep rules or Garak plugins to the community
    • Prepare for interviews by practicing scenario-based AI security assessments and threat modeling exercises
    Resources
    • Open-source LLM applications on GitHub for audit practice (e.g., AutoGPT, OpenDevin, privateGPT)
    • Bug bounty programs on HackerOne or Bugcrowd that include AI scopes
    • AI security community channels - OWASP AI Security Project, AI Village at DEF CON
    Milestone

    You have a portfolio of AI security reviews, community contributions, and demonstrated ability to assess production AI systems end-to-end.

Practice Projects

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

LLM Application Security Audit - Open-Source Chatbot

Intermediate

Select an open-source LLM chatbot (e.g., chatbot-ui, Open WebUI) and perform a comprehensive security code review covering prompt injection vectors, API key handling, authentication, output sanitization, and infrastructure configuration. Produce a formal security assessment report with findings, severity ratings, and remediation recommendations.

~25h
SAST on Python/TypeScriptOWASP LLM Top 10 applicationSecurity report writing

Custom Semgrep Rules for AI Vulnerability Patterns

Intermediate

Build a set of 10+ custom Semgrep rules targeting common AI security anti-patterns: unsafe pickle loading, prompt template injection, missing output filtering before HTML rendering, excessive tool permissions in LangChain agents, and unvalidated embedding inputs. Publish the ruleset as an open-source Semgrep registry.

~30h
Semgrep rule writingPattern matchingAI vulnerability taxonomy

RAG Pipeline Threat Model and Security Hardening

Advanced

Build a RAG application using LangChain, Pinecone, and OpenAI, then perform a full STRIDE-LLM threat model on the architecture. Implement security hardening: namespace isolation in vector store, input validation, output content filtering, prompt hardening against injection, and audit logging. Document the threat model and all mitigations.

~35h
Threat modelingRAG securityLangChain security patterns

CI/CD AI Security Pipeline with Garak Integration

Advanced

Design and implement a GitHub Actions CI/CD pipeline for an AI application that includes: Bandit for Python SAST, custom Semgrep rules for AI patterns, GitLeaks for secret scanning, Trivy for container scanning, and Garak for automated LLM vulnerability scanning against a staging endpoint. Publish the pipeline template as a reusable GitHub Action.

~30h
CI/CD securityGarak configurationAutomated security testing

LLM Agent Security Assessment - Tool-Calling Audit

Advanced

Analyze an open-source LLM agent framework (e.g., AutoGPT, CrewAI, OpenDevin) with focus on tool-calling security. Map all registered tools, assess permission boundaries, identify potential prompt injection to tool escalation paths, and produce a detailed security audit with proof-of-concept demonstrations for the top 3 findings.

~40h
Agent security analysisTool permission auditingProof-of-concept exploit development

AI Security Maturity Assessment Framework

Beginner

Research and compile an AI security maturity assessment framework with 5 maturity levels across dimensions like governance, threat modeling, secure development, testing automation, and incident response. Create a self-assessment questionnaire and scoring rubric that organizations can use to evaluate their AI security posture.

~20h
Security governanceRisk assessment methodologyAI compliance frameworks

Ready to Start Your Journey?

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

Practice Interview Questions Explore More Careers