Foundations of Prompt Security and Defense

As more applications integrate Large Language Models (LLMs), securing them against manipulation is no longer optional. An insecure prompt can lead to data leaks, harmful content, and unreliable system behavior, undermining user trust and creating significant risks. This course provides a foundational understanding of prompt security. You will learn to identify common vulnerabilities and implement effective defensive measures, enabling you to build safer and more robust AI-powered features. You'll gain the confidence to write more secure prompts and architect more resilient systems. What you'll learn: - Understand the core concepts of prompt hacking, including prompt injection and jailbreaking. - Learn defensive prompting techniques to create robust system instructions for AI models. - Practice identifying vulnerabilities in prompts and application designs through written exercises. - Explore methods for sanitizing inputs and validating model outputs to prevent malicious use. - Discover the unique security considerations for systems using Retrieval-Augmented Generation (RAG). - Apply a basic threat modeling framework to your own LLM-based projects. The course begins by explaining the common attack vectors and the mindset of an attacker. From there, we will walk through practical, text-based examples of defensive strategies you can apply to protect your applications. This course is designed for beginners. No prior experience in cybersecurity is required, but a basic familiarity with LLMs is helpful. It's ideal for developers, product managers, and technical leaders looking to secure their AI integrations. Start learning how to build more secure and trustworthy AI applications today.