With an understanding of how attackers target Large Language Models, the focus now shifts to constructing effective defenses. This chapter introduces practical strategies and techniques for mitigating vulnerabilities and enhancing the security of LLM systems. We will move from identifying weaknesses to implementing protective measures.

The sections ahead detail both proactive and reactive measures. Specifically, we will cover:

• Methods for input validation and sanitization to block malicious prompts.
• Techniques for output filtering and content moderation to control LLM responses.
• The application of adversarial training and specialized fine-tuning to build more resilient models.
• Using instruction tuning for safety alignment to guide LLM behavior toward safer and more aligned responses.
• Strategies for model monitoring and anomaly detection to identify unusual or malicious activities.
• Implementing rate limiting and access controls for LLM APIs to prevent abuse.
• Approaches for detecting jailbreak attempts aimed at bypassing safety protocols.
• Holistic approaches to strengthening overall LLM system defenses. The chapter concludes with a hands-on exercise where you'll implement a basic input sanitizer in Python.