While previous chapters focused on steering Large Language Models (LLMs) towards desired behaviors, this chapter addresses the security aspects inherent in deploying these powerful systems. Even models aligned through techniques like RLHF can exhibit vulnerabilities when faced with specifically crafted inputs designed to circumvent safety measures or cause unintended actions. Understanding these potential failure points is essential for building dependable AI applications.

Here, you will learn to:

• Categorize and identify different types of adversarial attacks against LLMs, such as jailbreaking and prompt injection.
• Analyze how data poisoning during training can compromise model integrity.
• Recognize privacy implications like membership inference attacks.
• Implement defensive strategies, including adversarial training to improve model resilience.
• Apply input sanitization and output filtering techniques as practical safeguards.
• Understand the current state and limitations of formal verification methods for LLM safety.

We will examine the mechanisms behind these attacks and the practical steps involved in constructing defenses, equipping you to develop more secure LLM systems.