If you learn AI Security Engineering, you will never be unemployed again. Stage 1: Threat Modeling & Foundations - Learn: OWASP Top 10 for LLMs, MITRE ATLAS, STRIDE, trust boundaries. - Practice: Threat model a RAG chatbot and map every place untrusted text enters the system. Stage 2: Prompt Injection & Jailbreaks - Learn: Direct vs indirect injection, instruction hierarchy, multi-turn attacks. - Practice: Hijack a sandboxed agent with a poisoned webpage, then patch it. Stage 3: Insecure Output Handling - Learn: LLM-driven XSS, SQLi, SSRF, command injection, excessive agency. - Practice: Exploit an app that pipes model output into a shell, then fix it with strict validation. Stage 4: RAG & Vector DB Security - Learn: Embedding poisoning, tenant isolation, ACL-aware retrieval. - Practice: Plant a malicious document in a knowledge base and make the bot leak another user's data. Stage 5: Agent & Tool Security - Learn: Least privilege, confused deputy attacks, sandboxing, scoped credentials. - Practice: Give an agent shell and email tools, then build a permission layer that blocks dangerous chains. Stage 6: MCP Security - Learn: Tool poisoning, rug pulls, tool shadowing, OAuth scopes, server authentication. - Practice: Build a malicious MCP server that exfiltrates data, then write a scanner that catches it. Stage 7: Data Leakage & Privacy - Learn: PII exposure, training data extraction, membership inference, DLP. - Practice: Extract memorized strings from a fine-tuned model and measure the leakage rate. Stage 8: Model Supply Chain - Learn: Pickle deserialization, safetensors, model signing, AI-BOM, typosquatting. - Practice: Craft a malicious model file that executes code on load, then build a pipeline that refuses it. Stage 9: Adversarial ML - Learn: Evasion, data poisoning, backdoors, model extraction. - Practice: Backdoor a small classifier with 1% poisoned data and detect it afterward. Stage 10: Guardrails & Runtime Enforcement - Learn: Input/output classifiers, policy engines, deterministic checks, human-in-the-loop. - Practice: Put a policy gateway in front of an agent that blocks risky actions before they execute. Stage 11: Red Teaming & Evals - Learn: garak, PyRIT, promptfoo, attack success rate, regression suites. - Practice: Automate a red-team pipeline in CI that fails the build when a new jailbreak lands. Stage 12: Detection, IR & Governance - Learn: Audit logging, anomaly detection, NIST AI RMF, ISO 42001, EU AI Act. - Practice: Write an incident response playbook for a compromised agent, then run a tabletop exercise. Chatbots are a commodity. AI Security is the physics of trust. The modern AI Security Engineer builds the immune system for autonomous intelligence. Bookmark and Repost!