Smøl🔺️høDlŕ🧨💥

If you learn AI Security Engineering, you will never be unemployed again. I’ll do this. Stage 1: AI Attack Surfaces & Threat Modeling - Learn: OWASP Top 10 for LLMs, STRIDE for AI, the "Confused Deputy" problem in agentic workflows & data flow mapping. - Practice: Draw a complete threat model for an AI agent that reads emails and writes to a CRM, identifying every indirect injection and data exfiltration vector. - Why: You cannot secure an agentic workflow if you don't understand how an attacker can hijack the agent's legitimate privileges. Stage 2: Advanced Prompt Injection & Jailbreaking - Learn: Direct vs. Indirect injection, Crescendo attacks (multi-turn grooming), payload splitting and encoding evasion (Base64, ROT13). - Practice: Build an automated harness that successfully bypasses a commercial LLM's system prompt using a multi-turn Crescendo strategy. - Why: Basic "ignore previous instructions" filters are trivial to bypass. Modern attacks groom the model's context over multiple turns. Stage 3: Agentic Security & MCP Tool Poisoning - Learn: Model Context Protocol (MCP) security, JSON-RPC sanitization, shadow tool execution and least-privilege scoping for agents. - Practice: Write an MCP server that strictly validates incoming tool parameters via Pydantic and blocks "confused deputy" attacks where a user prompt tries to access admin tools. - Why: Agents are only as secure as the tools they call. Tool poisoning is the SQL Injection of the Agentic Era. Stage 4: RAG Security & Data Poisoning - Learn: Vector DB access control, citation spoofing, shadow context injection and row-level security for semantic search. - Practice: Inject a "poisoned" document into a RAG pipeline that forces the LLM to output a specific malicious URL when queried about company policy. - Why: If an attacker can slip a poisoned document into your knowledge base, they own every answer the AI generates. Stage 5: PII, Privacy & Cryptographic Guardrails - Learn: Deterministic masking, UUID swapping, differential privacy and pre-flight/post-flight unmasking proxies. - Practice: Build a middleware proxy that intercepts user prompts, swaps SSNs and credit cards for UUIDs before the LLM sees them and restores them post-generation. - Why: Compliance teams will kill any AI product that sends raw customer PII to a third-party frontier API. Stage 6: Identity, Auth & Delegated Access for Agents - Learn: OAuth2 for machine identities, scoped JWTs, human-in-the-loop (HITL) approval gates and agent-to-agent (A2A) trust boundaries. - Practice: Build an auth broker where an AI agent acts on a user's behalf using a heavily scoped, time-limited OAuth token, rather than holding the user's raw password. - Why: Agents need hands but they should never hold the master keys to the enterprise. Stage 7: Output Validation & Grammar-Constrained Guardrails - Learn: CFG (Context-Free Grammar) decoding, NeMo Guardrails, output sanitization and regex-enforced JSON schemas. - Practice: Implement a guardrail layer that physically prevents the LLM from generating markdown links or executable code blocks in its final output. - Why: You cannot rely on the system prompt to "please don't output bad things." You must enforce it at the token-decoding level. Stage 8: Automated Red Teaming & Fuzzing - Learn: Microsoft PyRIT, Garak, LLM-based fuzzing, adversarial dataset generation and CI/CD security gates. - Practice: Build a CI pipeline using PyRIT that automatically attacks your agent with 1,000 adversarial payloads on every PR, blocking merges if the jailbreak rate exceeds 1%. - Why: Manual red teaming doesn't scale. You need automated, continuous adversarial fuzzing integrated directly into your deployment pipeline. Stage 9: Model Extraction & IP Protection - Learn: Membership inference attacks, model inversion, API rate-limiting by semantic similarity and output watermarking. - Practice: Build an API gateway that detects and blocks scraping bots trying to extract your fine-tuned model's weights via thousands of systematic, low-temperature queries. - Why: Your fine-tuned model's weights are your IP. Competitors will try to distill your model through the public API. Stage 10: Mechanistic Interpretability & Alignment Safety - Learn: Activation patching, logit lens, circuit discovery, detecting reward hacking and tools like TransformerLens. - Practice: Locate the specific attention heads in an open-source model responsible for "deception" or "sycophancy" and patch their activations to neutralize the behavior. - Why: You cannot fix hidden biases or misalignment if you treat the neural network as a black box. Stage 11: Compliance, Auditing & Governance - Learn: EU AI Act requirements, NIST AI RMF, immutable audit trails, SOC2 for AI and bias testing frameworks. - Practice: Build an immutable, append-only audit log that records the exact prompt, retrieved context, tool calls and final output for every single agent action. - Why: When an AI agent makes a mistake that causes financial or legal damage, the enterprise must be able to prove exactly why it happened. Stage 12: Public CVEs, Teardowns & Portfolios - Learn: Responsible disclosure, writing security advisories, publishing threat models and bug bounty hunting for AI. - Practice: Find a vulnerability in an open-source AI framework (like LangChain or an MCP server), responsibly disclose it and publish a teardown of the exploit chain. - Why: AI Security is a brand new field. Publishing novel attack vectors and defenses makes you the undisputed authority in the space. Bookmark and Repost!