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AI Security Masterclass Prompt Injection & LLM Security
Published 7/2026
Created by Arjun Vaid, School of AI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 87 Lectures ( 11h 28m ) | Size: 9.5 GB
Build, attack, and secure LLM, RAG, tool-calling, memory, and AI agent applications with Python and Ollama.
What you'll learn
⚡ Python developers who want to build secure LLM, RAG, and AI agent applications.
⚡ AI engineers and GenAI developers looking to defend their applications against prompt injection, jailbreaks, and other LLM attacks.
⚡ Software engineers building AI-powered products with OpenAI, Ollama, or other large language models.
⚡ RAG and AI agent developers who want to secure document retrieval, tool calling, memory, and multi-agent workflows.
⚡ Cloud and backend developers integrating AI into production applications who need practical security patterns and guardrails.
⚡ Technical architects and engineering leads responsible for designing secure AI systems and governance.
⚡ Students and developers who already understand basic Python and want to specialize in AI application security.
⚡ Anyone interested in AI security who prefers hands-on labs over theory and wants to learn by building, attacking, and securing real applications.
Requirements
❗ Basic Python programming knowledge, including variables, functions, classes, and modules.
❗ A computer running Windows, macOS, or Linux with permission to install software.
❗ No prior knowledge of AI security, cybersecurity, or prompt injection is required-we'll cover everything step by step.
❗ Basic understanding of Large Language Models (LLMs) or AI chatbots is helpful but not mandatory.
❗ An OpenAI API key is optional. Throughout the course, you'll also learn how to use free local models with Ollama to complete the labs.
❗ A willingness to build, break, and secure real AI applications through hands-on coding exercises.
Description
This course contains the use of artificial intelligence
Build AI applications that are not only powerful, butsecure by design.
In this hands-onAI Security Masterclass, you will learn how to build, attack, and secure modern applications powered byLarge Language Models (LLMs). UsingPython andOllama, 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 whyLLM security differs from traditional application security. You will then launch realisticprompt injection attacks, attempt to expose hidden instructions, test role-playing and multi-turnjailbreaks, and experiment with encoded prompt manipulation techniques.
Next, you will build a document-basedRAG 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 atool-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, andhuman-in-the-loop approval for sensitive actions.
You will also addpersistent 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 autonomous, you will build anAI agent capable of planning tasks, selecting tools, and executing multi-step workflows. You will then attempt to compromise its decisions and secure it with permissions, policy enforcement, approval gates, and constrained execution.
By the end of the course, you will combine every defense into a reusableAI Security Gateway that includes
✨Prompt injection detection
✨Jailbreak filtering and risk scoring
✨RAG security and context sanitization
✨Tool authorization and parameter validation
✨Memory poisoning protection
✨Agent security controls
✨Human approval workflows
✨Output validation and audit logging
Finally, you will package and run the complete secure AI assistant usingDocker and connect it to local models throughOllama.
This course is ideal forPython developers,Generative AI engineers,RAG developers,AI agent builders, software engineers, and technical professionals who want practical experience securing real LLM applications.
You will not simply study AI attacks. You will build vulnerable features, exploit them, implement defenses, and verify that those defenses work.
Build it. Attack it. Secure it.
Who this course is for
⭐ Python developers building AI-powered applications
⭐ AI and Generative AI engineers
⭐ Developers working with OpenAI, Ollama, or other LLM providers
⭐ Engineers building RAG systems, AI agents, and tool-calling applications
⭐ Software architects and technical leads designing production AI systems
⭐ Students and developers looking to specialize in AI application security
⭐ Anyone who prefers learning through hands-on labs by building, attacking, and securing real AI applications
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