The OSAI study notes were created to be a companion set of notes while you prepare for OSAI exam. These notes match the syllabus that you follow while you attend the official course.
It is also created to bridge the gap between traditional penetration testing and modern AI security. Rather than treating AI as a theoretical discipline, this guide approaches it through the mindset of an offensive security practitioner. Every chapter focuses on how AI systems are built, where they fail, how attackers abuse them, and how those weaknesses can be identified, exploited, and documented during realistic security engagements.
Authored and reviewed by certified professionals with firsthand experience preparing for and earning the certification.
Who Is This Handbook For?
The OSAI Handbook is written for security professionals who already understand the fundamentals of offensive security and want to develop practical expertise in attacking AI-powered systems.
It is particularly valuable for penetration testers looking to expand into AI security, red team operators assessing enterprise AI deployments, bug bounty hunters encountering LLM-enabled applications, application security engineers reviewing AI integrations, SOC analysts seeking to understand modern AI attack techniques, and cybersecurity students preparing for the OSAI certification.
The handbook assumes familiarity with networking, web applications, APIs, Linux, Windows, and common penetration testing concepts. Instead of teaching those fundamentals from scratch, it builds on them by demonstrating how traditional offensive techniques evolve when the target becomes an AI application, an autonomous agent, a RAG pipeline, or an entire AI infrastructure stack.
Table of Contents:
Exam Preparation
OSAI and the Offensive Security Approach to AI Systems
AI/ML Fundamentals for Attackers
LLM Architecture Deep Dive
Introduction to Red Teaming AI Systems
AI Red Teaming Methodology
AI Application Attacks
AI Recon
Attacking AI Agents
RAG Pipeline Attacks
Attacking Model Context Protocol (MCP)
Attacking Embeddings
Agent & Tool Hijacking
Multi-Agent Exploitation
MAESTRO Threat Modeling
AI Infrastructure Attacks
Model Extraction
Data Poisoning
AI Supply Chain Attacks
AI Infrastructure & Deployment Exploits
AI Infrastructure Attack Surface
AI Infrastructure Recon
API & Endpoint Attacks
Model Serving Exploits
ML Framework Exploitation
Container & GPU Workload Escapes
Practical Methodology
Offensive Methodology Recap
Case Studies & Recent CVEs
Practical Scenarios
Building an AI Red Team Lab
AI Threat Modeling
Exam Cheatsheet
Reporting & Documentation
Page Count: 153
Format: PDF
How to Get the AI Red Teaming Notes
You can get the notes from this link
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