With an understanding of potential LLM vulnerabilities and attack surfaces, this chapter details the primary methods for actively testing these models. We will cover a range of techniques that are central to LLM red teaming operations.
You will learn to apply both manual and automated testing strategies. This includes crafting adversarial prompts, using automated generation and fuzzing techniques, and working with open-source red teaming tools. The chapter also examines persona-based testing to simulate varied attacker profiles, methods for assessing multi-turn conversational weaknesses, and techniques for identifying bias or harmful content generation. Practical exercises will guide you in applying these adversarial methods.
3.1 Manual Adversarial Prompt Crafting
3.2 Automated Prompt Generation and Fuzzing
3.3 Utilizing Open-Source Red Teaming Tools
3.4 Persona-Based Testing: Simulating Malicious Actors
3.5 Multi-Turn Conversation Attacks
3.6 Exploiting LLM Memory and Context Windows
3.7 Identifying Bias and Harmful Content Generation
3.8 Semantic Similarity for Evasion
3.9 Hands-on: Crafting Adversarial Prompts