LLM Application Security: Defending Against Prompt Injection and Vulnerabilities

As Large Language Models (LLMs) become central to modern software, they introduce entirely new security attack vectors that traditional firewalls cannot block. Understanding how to secure these AI-driven applications is no longer optional—it is a critical requirement for modern developers and security professionals. This text-based course guides you through the unique security landscape of LLM integrations. You will transition from understanding basic AI architectures to identifying vulnerabilities and implementing robust defense mechanisms to protect your applications from malicious actors. What you'll learn: - Understand foundational LLM security concepts and the unique vulnerabilities of generative AI models. - Analyze common attack vectors including prompt injection, jailbreaking, and indirect injection. - Identify risks associated with data leakage, training data poisoning, and model inversion. - Implement defense strategies like input sanitization, safe system prompt design, and output validation. - Explore modern security frameworks, including the OWASP Top 10 for LLM Applications and AI guardrails. You will start with core definitions and security terminology before moving into practical code-based scenarios and defensive configurations. The material guides you through realistic attack simulations and step-by-step remediation strategies. This course is designed for software developers, security analysts, and AI enthusiasts who are new to LLM security, with no prior background in artificial intelligence security required. Start reading today to build secure, resilient LLM applications that protect user data and system integrity.