Udemy - LLM and API Security - AI Penetration Testing Bootcamp torrent

LLM & API Security: AI Penetration Testing Bootcamp
https://WebToolTip.com
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.