Course Overview
This course equips professionals with practical skills to identify, exploit, and defend against vulnerabilities in modern AI and LLM systems. As artificial intelligence becomes embedded in enterprise applications, the attack surface grows dramatically — from prompt injection and data poisoning to autonomous agent abuse and MCP exploitation.
Gain hands-on expertise in securing AI applications, understanding emerging threats, and implementing enterprise-grade defenses. The course spans LLM fundamentals, real-world attack techniques, and cutting-edge topics including Model Context Protocol (MCP) security, RAG architecture risks, and AI-powered pentesting automation.
Prerequisites
Basic understanding of web application security concepts (HTTP, APIs, common vulnerabilities) and familiarity with Python or any scripting language is recommended. No prior AI/ML expertise is required — the course begins with LLM fundamentals before progressing to offensive techniques.
Training Modules
Understand how LLMs work internally including tokens, transformers, and inference flow. Build a strong base for AI security concepts.
Explore the major risks identified in LLM applications and how they impact enterprise systems.
Hands-on setup of a working lab to safely test AI vulnerabilities and attacks.
Learn how attackers manipulate prompts and how to design defenses against them.
Understand hidden and multi-layer prompt injection methods used in real-world attacks.
Analyze how AI agents can be abused to perform unintended actions.
Identify how AI generates incorrect outputs and the business risks involved.
Learn how data leaks occur and how to prevent exposure of confidential information.
Understand risks from third-party models, APIs, and integrations.
Explore how training data can be manipulated to compromise AI behavior.
Learn how improper handling of AI outputs can lead to vulnerabilities.
Understand how attackers extract hidden system instructions.
Learn risks in embeddings and vector search systems.
Compare Retrieval-Augmented Generation with traditional LLM models.
Design scalable and secure AI chatbot systems.
Understand how attackers extract sensitive data from AI systems.
Implement validation strategies for secure AI inputs.
Explore injection risks in UI layers interacting with AI.
Understand command-level attacks triggered via AI.
Learn how hidden metadata can leak sensitive information.
Understand how attackers alter AI memory behavior.
Introduction to MCP and its role in AI ecosystems.
Practical setup for testing MCP-related vulnerabilities.
Breakdown of MCP architecture and modules.
Capture and analyze MCP communication.
Understand exploitation techniques targeting MCP tools.
Compare attack strategies on different resource types.
Identify risks of internal data leaks.
Explore command injection specific to MCP.
Understand misuse of MCP resources.
Learn how attackers override tools in MCP.
Techniques to evade MCP defenses.
Understand session compromise methods.
Learn how AI can be used to execute malicious code.
Identify access control flaws in AI applications.
Deploy and manage local LLMs securely.
Understand steps to create custom AI models.
Use AI within security testing workflows.
Automate security testing using AI tools.
Enhance developer productivity using AI assistants.
Apply all learned techniques to identify and exploit chatbot vulnerabilities.