Hands On AI (LLM) Red Teaming

About this course

Objective

This course provides hands-on training in AI security, focusing on red teaming for large language models (LLMs). It is designed for offensive cybersecurity researchers, AI practitioners, and managers of cybersecurity teams. The training aims to equip participants with skills to:

• Identify and exploit vulnerabilities in AI systems for ethical purposes.
• Defend AI systems from attacks.
• Implement AI governance and safety measures within organizations.

Learning Goals

Understand generative AI risks and vulnerabilities.

Explore regulatory frameworks like the EU AI Act and emerging AI safety standards.

Gain practical skills in testing and securing LLM systems.

Course Structure

Introduction to AI Red Teaming:

• Architecture of LLMs.
• Taxonomy of LLM risks.
• Overview of red teaming strategies and tools.

Breaking LLMs:

• Techniques for jailbreaking LLMs.
• Hands-on exercises for vulnerability testing.

Prompt Injections:

• Basics of prompt injections and their differences from jailbreaking.
• Techniques for conducting and preventing prompt injections.
• Practical exercises with RAG (Retrieval-Augmented Generation) and agent architectures.

OWASP Top 10 Risks for LLMs:

• Understanding common risks.
• Demos to reinforce concepts.
• Guided red teaming exercises for testing and mitigating these risks.

Implementation Tools and Resources:

• Jupyter notebooks, templates, and tools for red teaming.
• Taxonomy of security tools to implement guardrails and monitoring solutions.<

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