Understanding Prompt Injection: Securing LLM Applications

Large language models are transforming how we build software, but they also introduce entirely new security risks. As developers and AI enthusiasts adopt these systems, understanding how malicious prompts can hijack application behavior is critical to keeping data safe.\n\nThis course provides a clear, conceptual foundation in prompt injection attacks and defenses. You will transition from understanding basic prompt structures to identifying vulnerabilities, analyzing real-world attack vectors, and implementing modern defensive design patterns.\n\nWhat you'll learn:\n- Understand the core mechanics of prompt injection and how large language models process instructions.\n- Distinguish between direct system overrides and indirect injection attacks via external data sources.\n- Identify common vulnerabilities in LLM-powered applications, including data exfiltration and unauthorized tool execution.\n- Apply modern defensive strategies, such as structured outputs, system prompt isolation, and input filtering.\n- Align your security practices with industry standards, including the OWASP Top 10 for LLM Applications.\n- Practice evaluating prompt security through conceptual scenarios and structured text-based exercises.\n\nThe course starts with essential definitions of LLM behavior before guiding you through attack simulations and defensive design strategies. You will build a conceptual toolkit for auditing and securing AI applications without needing previous security experience.\n\nDesigned for software developers, product managers, and AI beginners, this course requires no prior cybersecurity background.\n\nBegin reading to secure your AI integration against modern prompt exploits.