Securing LLM Applications: Prompt Injection and Guardrails

As Large Language Models (LLMs) become central to modern software, securing them against emerging threats is a critical skill. This foundational course introduces AI security concepts tailored for backend developers and security enthusiasts. You will explore the unique vulnerabilities of LLM-integrated systems and learn practical techniques to defend against them. What you will learn includes how to: understand essential LLM architecture concepts and common AI security risks; identify and mitigate prompt injection and jailbreaking attacks; implement robust guardrails to filter inputs and control model outputs; secure Retrieval-Augmented Generation (RAG) pipelines and vector databases against data leakage; apply data protection principles to handle sensitive user information safely; and practice secure prompt engineering techniques to minimize unintended model behavior. The course begins by establishing key terminology and basic concepts of AI security before advancing to practical defense strategies. You will read through clear explanations and analyze written code snippets that demonstrate how to build secure backend integrations for LLMs. Designed for beginners and transitioning backend engineers, this program requires no prior cybersecurity expertise. Start learning today to build secure and resilient AI applications.