Secure Agentic AI: Stop Prompt Injection

Published 9/2026
Created by Bayt Al Hikmah
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 112 Lectures ( 24h 45m ) | Size: 1.2 GB

What you'll learn
Architect a secure agentic pipeline from zero
Attack your own agent safely with a real threat taxonomy
Design least-privilege tool execution: a reviewed tool registry, strict argument validation, path-traversal defense, and a centralized executor
Build a graph-based agent state machine with explicit planning, risk-routing, and human-approval nodes
Defend RAG and memory against poisoning
Enforce zero-trust authorization
Instrument the pipeline for real observability
Secure the supply chain
Engineer for resilience
Deliver a capstone-grade Sovereign Secure Agentic Pipeline

Requirements
Knowledge: Basic Python (functions, running scripts) and basic terminal comfort. No prior AI agent, LLM, or security experience required — Module 1 builds everything from a bare workstation up. No prior exposure to LangGraph, MCP, or agent frameworks needed — this course builds the state-machine and tool-safety patterns from first principles in plain Python before naming the frameworks that implement them at scale. No prior Kubernetes or DevSecOps experience required — Module 8 builds those skills using a local Kind cluster. Software (all free/open-source): Docker and Docker Compose, Git, Python 3.12. Python packages installed via pip in Lab 3: FastAPI, Uvicorn, Pydantic, pytest,httpx — all free and open-source. kubectl and kind for Module 8's Kubernetes labs — a lightweight local cluster, not a cloud account. Open-source security tooling used via Docker containers: OPA, Syft, Trivy, Keycloak — no licenses or ρáíd platforms required. No ρáíd AI API required. The entire course runs on a deterministic mock model adapter so every attack, defense, and test is fully reproducible without spending money on model calls. An optional Ollama-compatible local model can be swapped in later without changing any of the security architecture. Hardware: 10GB+ free disk space, 8GB+ RAM recommended (Docker containers plus an optional local Kubernetes cluster). No cloud account, no production AI system, and no real sensitive data required — every lab uses synthetic documents, synthetic tenants, and self-contained attack fixtures you build yourself.

Description
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 ρá†ch 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 ρáíd API), and then — deliberately — your first direct prompt injection, followed by indirect injection through a poisoned document, encoded payloads that evade naive keyword filters, and tool-description (metadata) injection, all captured as executable JSONL fixtures and organized into a formal attack taxonomy matrix.

Module 3 builds the defenses that actually matter: strict Pydantic request/response schemas, input normalization (Unicode, HTML entities), a transparent (and honestly limited) risk scorer, blocking high-risk input before model execution, and — the real core of tool safety — a reviewed tool registry, strict argument validation, path-traversal defense, and a centralized safe tool executor that no agent code can bypass.

Module 4 moves to orchestration: an explicit graph-based agent state, a planning node that separates risk assessment from execution, a response node that skips model calls entirely for risky requests, a human-approval review queue with idempotency keys, and full regression tests for both the "allow" and "needs review" paths.

Module 5 is where most agent security stops short and this course keeps going: a local knowledge base with a deliberately poisoned document, retrieval provenance hashing, context risk scoring applied toretrieved text with the same skepticism as user input, quarantine of risky retrieved context, and tenant-isolated memory paths with a dedicated path-traversal test — because RAG and memory are exactly the channels where indirect injection lives.

Module 6 builds real zero-trust authorization: identity attributes on every request, role-based tool policy, OPA/Rego default-deny policy-as-code evaluated in Docker, tenant isolation rules, and an OIDC/Keycloak deployment placeholder — with the course being explicit about the residual risk that client-supplied roles aren't trustworthy until real identity federation is wired in.

Module 7 makes the whole system observable and testable: structured JSON risk-decision logging, OpenTelemetry instrumentation, a Prometheus metrics foundation, a red-team regression runner that turns every attack fixture into a pass/fail gate, a promptfoo-style evaluation manifest, and an incident evidence bundle script.

Module 8 hardens the supply chain and deployment: a production Dockerfile, non-root container hardening, a CycloneDX SBOM via Syft, Trivy vulnerability scanning, Kubernetes deployment on a local Kind cluster, NetworkPolicy segmentation, and a Helm chart skeleton.

Module 9 proves the system survives real operational pressure: rate-limiting design and implementation, a circuit breaker for model calls, a response-caching boundary, a Redis placeholder for production-scale state, backup and restore drills, and a resilience runbook covering five distinct failure modes.

Module 10 closes with sovereignty and governance: documented sovereign deployment requirements, a local-first model provider configuration that defaults to blocking external APIs, data-residency metadata on every knowledge-base document, a compliance evidence matrix mapping technical controls to governance expectations, and a decentralized research-packet format for sharing reproducible findings without leaking raw sensitive payloads.

The Climax: Lab 100 — The Sovereign Secure Agentic Pipeline
Lab 100 runs full validation across the entire system: tests pass, the attack-regression suite exits clean, backup and restore drills succeed, and the evidence bundle is generated — then tags a final capstone release. The required demonstrations are concrete and non-negotiable: a direct prompt injection gets blocked or reviewed, a poisoned retrieved document gets quarantined, a path-traversal attempt against a tool gets blocked, a cross-tenant memory access attempt gets blocked, and OPA policy denies unauthorized tool use. This isn't a certificate exercise. It's proof that you built a layered defense where no single control is treated as sufficient.

Why Enroll Now
The market signal here is sharp and current: 2026 job postings for AI engineering roles increasingly combine software engineering, security architecture, and orchestration — the strongest candidates aren't the ones who can write a clever prompt, they're the ones who can design secure agent boundaries, enforce least privilege, and explain the blast radius of an automated action. This is one of the only courses that builds every layer of that defense yourself, from first principles, entirely on free and open-source tooling — no ρáíd AI API, no vendor lock-in, and a working system you can point directly at your own agent stack.

Who this course is for
The AI/ML Engineer Shipping Their First Agent to Production You've built an agent with LangGraph or a similar framework and it works great in the demo — until someone pastes "ignore previous instructions" into a support ticket it summarizes. This course builds the exact architecture-level defenses production agent systems need: least-privilege tools, context quarantine, and human approval gates, using patterns transferable directly into whatever framework you already use.
The AppSec/Security Engineer Moving Into AI Security You know threat modeling and secure architecture, but "prompt injection" still feels like a fuzzy, unsolved problem. This course treats it exactly as it should be treated: an architecture problem, not a prompt-writing problem. You'll build a real attack taxonomy (direct, indirect, encoded, tool-metadata) as executable regression tests, then design the layered controls — validation, policy-as-code, quarantine — that actually mitigate each vector.
The Platform Architect Designing Sovereign, Regulated AI Systems You're responsible for deploying AI agents in an environment where GDPR, DORA, or the EU AI Act aren't optional — and "we'll add security later" isn't acceptable when agents can call tools and touch real data. This course builds sovereignty into the architecture from Module 1: local-first model configuration, data-residency metadata, a compliance evidence matrix, and a capstone you could genuinely present to a governance review board.

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