Course Syllabus: Operational AI Red Teaming

Course Code: AIRT-404 Duration: 4 Weeks Level: Advanced Prerequisites: Python programming, Basic Pen Testing knowledge, API usage.

Course Description

This intensive four-week course is designed to transform security professionals into competent AI Red Teamers. Moving beyond theoretical "prompt engineering," this curriculum focuses on the engineering, swarming, and adversarial ML techniques required to secure enterprise-grade Artificial Intelligence systems. Students will engage in hands-on labs using Python, attacking local LLMs, and designing defense-in-depth architectures.

Learning Objectives

By the end of this course, students will be able to:

Analyze LLM architectures (Transformers, RAG, Agents) to identify attack surfaces.
Execute precision attacks including Prompt Injection, PII Extraction, and Indirect Injection.
Automate vulnerability discovery using Python scripts and fuzzing frameworks.
Design robust defense systems compliant with EU AI Act and NIST RMF.
Report findings effectively to executive and technical stakeholders.

Weekly Schedule

Week 1: Foundations & Architecture

The Physics of the Alien Mind

Topics: Transformer Architecture, Tokenization, Attention Mechanisms, Threat Modeling (STRIDE for AI).
Lab: "The Tokenization Gap" - Exploiting byte-pair encoding quirks.
Reading: Handbook Chapters 1-13.

Week 2: Core Offensive Techniques

Breaking the Guardrails

Topics: Direct Prompt Injection, Jailbreaking (DAN, Context Switching), Obfuscation, Automated Fuzzing.
Lab: "Automated Jailbreaker" - Writing a Python fuzzer for gpt-3.5-turbo.
Reading: Handbook Chapters 14-18, 32.

Week 3: Advanced Exploitation

Beyond the Chatbox

Topics: Indirect Prompt Injection (RAG Poisoning), Data Extraction, Supply Chain Attacks, Agent Exploitation (Confused Deputy).
Lab: "The Exploding Email" - Crafting an indirect injection payload.
Reading: Handbook Chapters 11-13, 19, 44.

Week 4: Defense, Governance & Operations

Closing the Loop

Topics: Blue Team Architecture (Guardrails, Firewalls), Compliance (EU AI Act), Remediation, Executive Reporting.
Lab: "Shields Up" - Designing a PII filter and rate limiter.
Capstone: Full "Paper Audit" of a hypothetical AI feature.
Reading: Handbook Chapters 36-46.

Technical Requirements

Hardware: Laptop with Python 3.8+ support. NVIDIA GPU recommended but not required (can use API).
Software: GitHub Client, VS Code, Burp Suite (Community).
API Access: OpenAI API Key (or local Ollama instance).

Assessment

Weekly Labs: 40% (Pass/Fail)
Final Capstone (Audit Report): 60%

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