AI Security Masterclass: Prompt Injection & LLM Security

About this course

Build AI applications that are not only powerful, but secure by design.

In this hands-on AI Security Masterclass, you will learn how to build, attack, and secure modern applications powered by Large Language Models (LLMs). Using Python and Ollama, you will create a complete AI assistant and progressively add chat, Retrieval-Augmented Generation (RAG), tool calling, persistent memory, autonomous agents, and production security controls.

You will begin by building a local AI chat assistant and exploring why LLM security differs from traditional application security. You will then launch realistic prompt injection attacks, attempt to expose hidden instructions, test role-playing and multi-turn jailbreaks, and experiment with encoded prompt manipulation techniques.

Next, you will build a document-based RAG application capable of answering questions from PDFs. You will see how malicious documents can introduce indirect prompt injection, poison retrieved context, manipulate answers, and undermine trusted knowledge. You will secure the RAG pipeline using source validation, context isolation, sanitization, and enforcement controls.

The course then moves beyond chatbot responses into real application actions. You will build a tool-calling AI assistant with weather, calculator, and email tools. You will test parameter injection and unauthorized tool execution before implementing input validation, least-privilege permissions, destination restrictions, and human-in-the-loop approval for sensitive actions.

You will also add persistent AI memory and learn how attackers can poison stored information to manipulate future conversations. You will protect memory using validation, approval workflows, risk scoring, and safe storage policies.

As the assistant becomes more autonom

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.