LLM & API Security AI Penetration Testing Bootcamp
Published 9/2026
Created by Bayt Al Hikmah
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 112 Lectures ( 26h 56m ) | Size: 1.2 GB
From prompt-injection novice to production AI security engineer: build, test, and harden 100 real-world LLM labs.
What you'll learn
Architect and deploy a full LLM application security range using FastAPI, Docker, and a mock LLM gateway from scratch. Map LLM attack surfaces, trust boundaries, and OWASP LLM Top 10 risks using MITRE ATLAS-aligned threat models. Execute and defend against direct and indirect prompt injection, system prompt leakage, and unsafe output rendering. Implement API authentication, JWT validation, tenant isolation, and rate limiting to stop unbounded LLM abuse. Build and secure a RAG pipeline: ingestion, embeddings, vector stores, metadata filtering, and retrieval poisoning defenses. Design least-privilege agentic tool systems with approval gates, sandboxing, and Model Context Protocol security reviews. Automate AI red teaming with garak, PyRIT, and custom fuzzing harnesses wired into CI-compatible security gates. Harden the software supply chain with Trivy scans, Syft SBOMs, Grype checks, and Cosign image signing plans. Deploy hardened workloads to Kubernetes with kind, admission policies, and SPIFFE/SPIRE workload identity design. Engineer observability, incident response, and cost-abuse controls, then ship a sovereign, audit-ready capstone system.Requirements
Basic comfort with the command line (running commands, navigating directories) - no prior security certification required. Working knowledge of Python fundamentals (variables, functions, basic scripting). You do not need prior FastAPI or API experience - it's taught inside the labs. A computer running macOS, Linux, or Windows with WSL2, with at least 8GB RAM (16GB recommended once you reach the Kubernetes and vector store modules) and 15GB of free disk space. Docker Desktop (or Docker Engine) and Git installed and working before Lab 001. Python 3.11 or newer installed locally. No cloud account, no paid API key, and no real production system is required or touched at any point - every lab runs against a local, intentionally vulnerable target you control. If you've never run docker --version before, that's fine. Lab 001 walks you through the entire pre-flight check line by line. You're not expected to arrive as a security expert - you're expected to leave as one.Description
This course contains the use of artificial intelligence.
We only charge a fee solely for the time invested in building this comprehensive curriculum.
Most people who work with LLM applications are tool installers. They can pull a Docker image, call an API, paste a jailbreak prompt they found on Twitter, and the result. That's a demo. It is not a defensible security assessment, and it will not survive five minutes of questioning from a CISO, a hiring manager, or a compliance auditor who asks: what exactly did you test, what did you not test, and how do you know your fix actually holds under regression?That gap - between "I tried a jailbreak" and "I can architect, test, harden, and govern a production LLM system"- is the entire reason this course exists. In 2026, LLM applications aren't chatbots anymore. They're customer service systems, financial workflows, health tooling, developer copilots, and agentic API orchestration layers with real data, real tool access, and real blast radius. The attack surface isn't just the prompt box. It's the API gateway, the identity layer, the retrieval pipeline, the tool registry, the deployment pipeline, and the audit trail behind all of it. Testing only the chat window is like inspecting a building by knocking on the front door and ignoring the wiring, plumbing, and fire exits.
This course fixes that with 100 sequential, hands-on labs- not slides, not theory dumps. Every lab follows the same zero-failure operating contract: pre-flight check, snapshot, build, verify, and rollback. You cannot break anything you can't immediately undo, which means you move fast without fear. Each lab explains the outcome you're aiming for, the mechanism behind it in plain language, exactly what success looks like on your screen, and three specific troubleshooting fixes when something goes sideways - because real engineers hit real friction, and pretending otherwise doesn't prepare you for production.
Here's the journey. You start by building your own authorized local LLM range - a FastAPI gateway, a mock model adapter, and your first deliberately vulnerable chat endpoint - and by Lab 010 you've already found and patched a real system-prompt disclosure bug with a passing regression test in hand. From there you build a full OWASP LLM-aligned threat model and MITRE ATLAS mapping, then spend a full module breaking and rebuilding prompt injection and output-handling defenses with real filters, structured JSON schemas, and PII redaction. You move into API security proper: JWT validation, tenant isolation rules, rate limiting, and audit ID design that would hold up in a real API security review. Then you build and attack a RAG pipeline - ingestion, embeddings, a live Qdrant vector store, retrieval poisoning simulations, and source allowlisting - before designing an agentic tool layer with least-privilege scopes, human approval gates, sandboxed execution, and a full Model Context Protocol security review. You automate all of it with garak, PyRIT, and a custom fuzzing harness wired into a CI-compatible security gate, then harden the supply chain with Trivy, Syft, Grype, and a Cosign signing plan before deploying to a local Kubernetes cluster with admission policies and SPIFFE/SPIRE workload identity. You finish with observability, incident response playbooks, chaos testing, and backup/restore proof - the operational muscle most AI security content skips entirely.
Then comes Lab 100. The capstone isn't a quiz. It's a sovereign-ready, signed evidence archive - capstone-evidence-lab100.tgz with a SHA-256 checksum - containing every architecture doc, every test result, a compliance evidence matrix, and an executive risk brief. A hiring manager or CISO can open that archive and see, line by line, what you tested, what you didn't, why you chose each control, and how the system fails safely under load. That's not a course completion artifact. That's a work sample.
Why now:2026's SOC environment, DORA resilience requirements, and the EU AI Act's human-oversight and logging obligations mean organizations can no longer treat LLM applications as unregulated experiments. Companies need engineers who can prove - with evidence, not vibes - that their AI systems are tested, governed, and recoverable. That's precisely the skill set this curriculum builds, lab by lab.
Lab 1 takes ten minutes: you'll scaffold your Git repo, confirm Docker and Python are ready, and commit your first clean snapshot. No cloud account, no real data, no risk to anything but your own local machine. Start there today - the capstone is 99 labs away, and every one of them is designed so you can't fail your way out of learning.
Who this course is for
Application security engineers and penetration testers who are confident testing traditional web apps and REST APIs but have never systematically tested an LLM-integrated system. The Goal: extend an existing AppSec skill set into prompt injection, RAG poisoning, and agentic tool abuse without starting from zero. The Need: a structured, lab-driven bridge from classic AppSec to AI-specific attack surfaces, backed by reproducible evidence rather than one-off jailbreak . Backend or platform engineers building LLM-powered products (chatbots, copilots, internal tools, agentic workflows) who are shipping features faster than they're securing them. The Goal: harden what they've already built and prove it to security, compliance, or leadership. The Need: concrete, implementable controls - output filtering, tenant isolation, tool approval gates, rate limits - mapped directly onto the FastAPI/Docker/Kubernetes stack they already use. Career-transitioning security professionals or senior students aiming for AI red team, AI security engineer, or platform security roles who keep seeing "LLM security," "OWASP LLM Top 10," and "MCP security" on job descriptions but have no portfolio proof of hands-on capability. The Goal: a defensible, interview-ready capstone project, not a certificate of attendance. The Need: 100 sequential labs that build toward one signed, checksum-verified capstone architecture they can walk a hiring panel through, control by control.Homepage
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