Securing AI Applications Against Prompt Injection

As businesses rapidly integrate Large Language Models into their software, securing these systems against malicious inputs has become a critical priority. Prompt injection vulnerabilities allow attackers to bypass safety guardrails, hijack system instructions, and compromise sensitive data. This text-only course guides you through the foundational security concepts of modern AI integrations. You will learn how attackers exploit prompt inputs, understand the mechanics of direct and indirect injection, and discover modern defensive patterns to protect your applications. What you'll learn: - Understand the core mechanics of prompt injection and how models process untrusted instructions. - Identify the difference between direct system overrides and indirect injection via external data sources. - Analyze common attack vectors, including unauthorized system actions and data exfiltration risks. - Apply robust prompt engineering defenses and secure system prompt design patterns. - Explore vulnerability landscapes specific to Retrieval-Augmented Generation (RAG) and autonomous AI agents. - Implement modern input validation, output filtering, and guardrail frameworks to secure user interactions. The course begins with essential terminology and the fundamental architecture of AI applications, before walking through practical scenario analyses and industry-standard defense strategies. Designed for software developers, product managers, and security beginners, this course requires no prior experience in cybersecurity or machine learning. Start reading today to build safer, more reliable AI-driven software.