Securing LLM Applications: Introduction to Vulnerabilities

Large Language Models (LLMs) are transforming software development, but they also introduce entirely new security vectors that traditional security frameworks cannot fully address. Understanding how to secure these AI-driven systems is now an essential skill for modern developers and security enthusiasts alike. This text-only course provides a clear, foundational path to understanding LLM vulnerabilities and securing your applications. You will transition from a beginner to a security-conscious developer capable of analyzing LLM risks and implementing robust defensive strategies. What you'll learn: - Understand fundamental LLM security concepts and the unique threat landscape of AI applications. - Identify common vulnerabilities such as prompt injection, training data poisoning, and model theft. - Analyze OWASP Top 10 risks specifically tailored for LLM applications. - Implement effective guardrails and sanitization techniques to prevent sensitive information disclosure. - Explore modern security patterns, including securing Retrieval-Augmented Generation (RAG) pipelines. - Practice threat modeling concepts to design safer AI integrations from the ground up. The course begins with essential terminology and the mechanics of how LLMs process instructions, then guides you through real-world vulnerability scenarios and modern defensive architectures. You will learn purely through written explanations, conceptual breakdowns, and practical code-based security patterns. This course is designed for software developers, security beginners, and technology enthusiasts who want to build safe AI applications, with no prior experience in cybersecurity or machine learning required. Start reading today to build secure, resilient, and trustworthy AI-powered systems.