Secure Agentic AI: Stop Prompt Injection

About this course

This course contains the use of artificial intelligence.

We only charge a fee solely for the time invested in building this comprehensive curriculum.

The "Just Add a Better System Prompt" Problem

Every team building AI agents eventually hits the same wall: an agent that reads documents, calls APIs, and takes actions is only as trustworthy as the least trustworthy thing it reads. Prompt injection isn't a bug you patch with a stronger system prompt — it's a fundamental consequence of mixing trusted instructions with untrusted content (user text, retrieved documents, web pages, tool descriptions, even the agent's own memory) inside a single context window. Hostile instructions can arrive from anywhere the agent looks, and no amount of "please ignore malicious instructions" phrasing closes that door reliably.

This course treats prompt injection as what it actually is: an architecture problem. You don't defend an agent by writing a cleverer prompt. You defend it by separating trust boundaries, validating everything at every boundary, giving tools the least privilege possible, and putting a human in the loop before anything consequential happens. Every one of those principles gets built, attacked, and tested in this course — not just described.

The Solution: 100 Labs, One Growing Secure Agent System

Every lab follows the same Zero-Failure framework: a pre-flight check, a Git-based safety net, and a documented rollback. By Lab 10 you'll have a working FastAPI agent with a model-adapter boundary, passing tests, and your first documented direct prompt-injection demonstration — a safe, working baseline before any real complexity is added.

What's InsideModules 1–2 build your workstation and a genuine attack taxonomy: a minimal agent API, a mock model adapter (so the entire course runs without a paid API), and then — deliberately — your first direct prompt injection, followe

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