Red Teaming LLM Applications: Security and Vulnerability Testing

As large language models become central to modern software systems, securing them against adversarial inputs and unintended behaviors is more critical than ever. This course introduces you to the essential practices of red teaming LLM applications, helping you find and patch security gaps before they reach production. You will start with core concepts of prompt injection, data poisoning, and model alignment, establishing a firm theoretical foundation before moving on to practical testing techniques. Through structured written lessons, you will learn how to simulate real-world attacks, evaluate model robustness, and implement defensive guardrails. What you'll learn: Understand the foundational security risks unique to LLM integrations, Identify and exploit prompt injection and jailbreaking vulnerabilities, Evaluate application safety using systematic quantitative and qualitative testing frameworks, Configure robust input and output guardrails to prevent data exfiltration, Implement modern defensive patterns and retrieval-augmented generation safety measures. This course starts with fundamental concepts and definitions, guiding you step-by-step through advanced evaluation workflows and defensive strategies. It is designed for software developers, security enthusiasts, and AI practitioners looking to build safer LLM integrations. No previous cybersecurity experience is required to begin.