//
A comprehensive, tier-based learning journey from foundational LLM security concepts through advanced red-teaming, compliance, and production operations. This path equips engineers, security practitioners, and product leaders with the knowledge to build, audit, and deploy LLM systems safely. Covers attack surfaces, defensive techniques, regulatory requirements, and operational practices.
A comprehensive, production-grade guide exploring every attack vector threatening LLM applications—from prompt injection and context poisoning to output exploitation and model theft. Covers real attack mechanisms, concrete risks across deployment stages, and layered defense strategies backed by OWASP frameworks and recent academic research.
Prompt injection occurs when untrusted user input rewrites your LLM instructions. Learn how it works across three attack depths, why implicit safeguards fail, and how to build prompts that resist exploitation.
Learn why hardcoding secrets in LLM prompts is catastrophically risky, and master three battle-tested patterns to keep credentials secure without sacrificing functionality.
Learn to architect prompts with three structural layers—role boundaries, explicit delimiters, and output schemas—so user input cannot become instructions. Built for engineers shipping production LLM systems.
Master five validation layers that catch semantic attacks regex can't. Learn when to validate before vs. after the model, tune for your risk tolerance, and implement a working pipeline today.
Learn to validate LLM outputs systematically—catching format errors, logical contradictions, and hallucinations without slowing your system to a crawl.
How to build retrieval-augmented generation systems that resist prompt injection, poisoned documents, and context manipulation through layered isolation strategies.
Learn systematic red-teaming techniques to find and fix vulnerabilities in your prompt-based systems before attackers do. This guide teaches you to think like an adversary—probing boundaries, testing jailbreaks, and exploiting context—so you can build defenses that stick.
Move beyond prompt engineering to constrain model behavior at inference time using token-level hardening, temperature tuning, and confidence thresholds.
Build detection systems that catch LLM attacks and anomalies in real time—without false-alarm fatigue. Learn what to log, which patterns signal trouble, and how to alert sustainably.
Every LLM deployment depends on a supply chain you don't fully control: a model provider, infrastructure, update cycles, and third-party tools. This guide maps that chain, shows you what to evaluate when choosing a model or API, explains the trade-offs between managed services and self-hosted deployments, and gives you a practical framework for making decisions that don't lock you in or expose you unnecessarily.
Tired of security conversations that happen at 3 AM during a production incident? This guide gives your team a concrete, copy-paste-ready security checklist for shipping LLM products safely. It covers five critical areas—from prompt injection to incident response—with clear sign-off criteria, escalation paths, and post-launch monitoring. By the end, you'll have a shared language for "ready to ship" that actually means something.
A practical guide for engineers and technical leaders building LLM systems in or for the EU. Cuts through regulatory language to explain the EU AI Act's risk classification system, compliance requirements, timeline, and what you actually need to do—documented with a realistic compliance scenario and actionable checklist.
To get the most from this path, you should have basic foundational knowledge about how LLMs work—specifically, the input-processing-output cycle and how prompts function as instructions. Familiarity with prompting concepts like system instructions, user input, and role-based prompts is important, and ideally you've already spent time writing and testing prompts in a real LLM tool like ChatGPT or Claude. On the technical side, comfort reading and writing code (or at minimum, following pseudocode) will help, along with some experience working with backend systems, APIs, and input validation patterns. You should also be comfortable with the basics of logging, monitoring, and alerting concepts.
310
Advanced