Learning Roadmap
How to Become a AI Vulnerability Assessment Specialist
A step-by-step, phase-based learning path from beginner to job-ready AI Vulnerability Assessment Specialist. Estimated completion: 7 months across 4 phases.
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Foundations: Security Meets Machine Learning
6 weeksGoals
- Understand core ML concepts: supervised learning, neural networks, transformers, fine-tuning, embeddings
- Learn the OWASP Top 10 for LLM Applications and MITRE ATLAS framework
- Set up a local lab environment with HuggingFace models and OpenAI API access
- Complete basic prompt injection challenges (e.g., Gandalf, Tensor Trust)
Resources
- Fast.ai Practical Deep Learning course (first 3 lessons)
- OWASP Top 10 for LLM Applications v2.0
- MITRE ATLAS website and case studies
- HuggingFace NLP course
- Gandalf by Lakera (interactive prompt injection game)
MilestoneYou can articulate the top 10 LLM vulnerability classes and have a working local environment for testing models.
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Core Skills: Adversarial Testing & Tooling
8 weeksGoals
- Master Garak and Promptfoo for automated LLM vulnerability scanning
- Learn ART (Adversarial Robustness Toolbox) for classical ML adversarial attacks
- Practice API security testing with Burp Suite or Caido against model endpoints
- Develop structured red-team test plans and documentation templates
Resources
- Garak documentation and GitHub repository
- Promptfoo documentation and example configs
- IBM ART tutorials and notebook examples
- PortSwigger Web Security Academy (API testing modules)
- Microsoft PyRIT repository and notebooks
MilestoneYou can independently run automated vulnerability scans against an LLM application and produce a structured report.
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Applied Red-Teaming: Full-Stack AI Assessment
8 weeksGoals
- Conduct end-to-end assessments of RAG pipelines, AI agents, and multi-modal systems
- Perform supply chain audits on model weights, datasets, and third-party components
- Execute privacy attacks: membership inference, model inversion, training data extraction
- Build a personal adversarial prompt library organized by attack taxonomy
Resources
- Anthropic's research on jailbreaking and constitutional AI
- Privacy attacks on ML models survey papers (Shokri et al., Carlini et al.)
- LangChain security documentation
- Cloud provider ML security whitepapers (AWS, Azure, GCP)
- NIST AI Risk Management Framework (AI RMF)
MilestoneYou can scope, execute, and deliver a complete AI vulnerability assessment for a production-grade LLM application.
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Specialization & Industry Authority
6 weeksGoals
- Deep-dive into a vertical specialization (financial AI, healthcare AI, autonomous systems, or agentic AI security)
- Contribute to open-source AI security tools or publish research on novel attack techniques
- Develop internal tooling or playbooks for repeatable assessments
- Build thought leadership through conference talks, blog posts, or bug bounty submissions
Resources
- Conference proceedings: IEEE S&P, USENIX Security, NeurIPS Trustworthy AI workshop
- HackerOne and Bugcrowd AI-focused programs
- Google Project Zero blog for methodology inspiration
- OWASP AI Security and Privacy Guide
- EU AI Act full text and compliance guides
MilestoneYou are recognized as a specialist who can lead AI security engagements, mentor junior assessors, and influence organizational AI security strategy.
Practice Projects
Apply your skills with hands-on projects. Ordered by difficulty.
LLM Chatbot Red-Team Playbook
BeginnerBuild a structured playbook of 50+ adversarial prompts targeting common LLM chatbot vulnerabilities (prompt injection, jailbreaking, information leakage, role confusion). Test against at least three different models and document success rates, categorizing findings by vulnerability class.
Automated LLM Vulnerability Scanner with Garak
IntermediateDeploy and customize Garak to scan an LLM endpoint for vulnerabilities. Write custom probes targeting application-specific attack surfaces (e.g., a customer support bot with refund capabilities), generate automated reports, and integrate the scanner into a CI/CD pipeline.
RAG Pipeline Security Audit Tool
IntermediateBuild a Python tool that audits a RAG pipeline for indirect prompt injection, chunk poisoning, retrieval manipulation, and context leakage. Include test cases that inject adversarial content into vector stores and measure whether the model follows injected instructions.
AI Agent Attack Simulation Framework
AdvancedDesign a framework that simulates adversarial interactions against an AI agent with tool-calling capabilities (database access, API calls, file operations). Map attack chains across multiple steps, test authorization boundaries, and generate risk-scored findings with remediation guidance.
Adversarial ML Robustness Benchmark
AdvancedUsing IBM ART and custom scripts, build a benchmark suite that evaluates image and text classification models against adversarial attacks (PGD, FGSM, C&W, textfooler). Compare robustness across model architectures, document certified robustness bounds, and publish results as a reusable evaluation framework.
AI Security Maturity Assessment Dashboard
IntermediateCreate an interactive dashboard (Streamlit or Gradio) that tracks an organization's AI security posture across models and features. Include metrics like vulnerability density, red-team coverage, mean remediation time, guardrail bypass rates, and compliance status against NIST AI RMF.
Ready to Start Your Journey?
Prep for interviews alongside your learning — it reinforces every concept.