Pentesting GenAI LLM models: Securing Large Language Models

Name: Pentesting GenAI LLM models: Securing Large Language Models
Rating: 4.1 (70 reviews)

Master LLM Security: Penetration Testing, Red Teaming & MITRE ATT&CK for Secure Large Language Models

Role Play

Created byStart-Tech Trainings, Start-Tech Academy

Last updated 4/2026

English

What you'll learn

Understand the unique vulnerabilities of large language models (LLMs) in real-world applications.
Explore key penetration testing concepts and how they apply to generative AI systems.
Master the red teaming process for LLMs using hands-on techniques and real attack simulations.
Analyze why traditional benchmarks fall short in GenAI security and learn better evaluation methods.
Dive into core vulnerabilities such as prompt injection, hallucinations, biased responses, and more.
Use the MITRE ATT&CK framework to map out adversarial tactics targeting LLMs.
Identify and mitigate model-specific threats like excessive agency, model theft, and insecure output handling.
Conduct and report on exploitation findings for LLM-based applications.

Course content

9 sections • 55 lectures • 3h 23m total length

Introduction and Course resource2:25

Why Benchmarks Are Not Enough: AI Safety & Security2:03
LLM Application Vulnerabilities - Demo 01 (Code Explanation)3:00
LLM Application Vulnerabilities - Demo 02 (Biased and Stereotypes)3:34
LLM Application Vulnerabilities - Demo 03 (Sensitive Data Disclosure)4:50
LLM Application Vulnerabilities - Demo 04 (Service Disruption)2:04
LLM Application Vulnerabilities - Demo - 05 (Hallucination)4:04
Foundation Models vs. LLM Apps2:37
Strategies for LLM Application Safety2:49
Examining LLM Vulnerabilities2:21
Identify major LLM risks—bias, privacy leaks, service disruption, and hallucinations. Apply defenses through filters, stress tests, and human-led policies to protect users and data.
Demo LLM Application Overview1:05
Explore a demo LLM application in a banking context, where a chatbot answers questions by retrieving documents and using an orchestrator to route queries, manage context, and log activity.
Importing the LLM Application0:52
About the upcoming roleplay0:38
Interview Simulation: Explaining LLM Security Concepts

What is ATT&CK?1:58
Understanding the Pyramid of Pain6:42
Overview of ATT&CK Matrices1:34
Explore MITRE ATT&CK matrices across enterprise, mobile, and ICS, with enterprise submatrices for Windows, Linux, Mac OS, and cloud services such as Azure, Android, Office 365, and Google Workspace.
ATT&CK Tactics11:02
ATT&CK Techniques3:37
ATT&CK Subtechniques4:30
Data Sources for ATT&CK1:58
Detection Strategies2:09
Implementing Mitigation Techniques2:21
Exploring ATT&CK Groups2:33
Software in the ATT&CK Framework1:57
Campaigns Overview2:29
ATT&CK Relationships2:23
ATT&CK Enterprise Matrix - Hands-On14:53
Quiz

Prompt Injection2:47
Examine prompt injection, a sneaky technique that tricks AI into revealing data or bypassing safety rules, with examples in chatbots, code generators, and other AI tools.
Indirect Prompt Injection: Demo12:50
Insecure Output Handling: Theory3:08
Insecure Output Handling: Demo11:02
Learn to test insecure output handling in ai systems by using indirect prompt injection to trigger cross-site scripting, demonstrating how an xss payload can delete a user account.
Supply Chain Vulnerabilities3:25
Identify and defend against supply chain vulnerabilities in software by vetting vendors, verifying updates, and monitoring for red flags across open source libraries, third party APIs, and cloud services.
Model Denial of Service (DoS)5:16
Stop Model DOS Attack2:10
Training Data Poisoning3:15
Sensitive Information Disclosure3:28
Plugin Security LLM Applications2:16
Understand how plugins extend large language models and reveal security risks like data leakage, remote code execution, and privilege escalation, caused by inadequate access controls and weak monitoring.
Excessive Agency2:44
Overreliance4:16
Model Theft2:33
Demonstrating Conceptual Clarity in LLM Security Penetration Testing
New AI Features in Pentesting GenAI LLM Models(The Latest Updates You Must Know)1:33
The final milestone!1:33

Requirements

Basic understanding of IT or cybersecurity Curiosity about AI systems and their real-world impact No prior knowledge of penetration testing or LLMs required

Description

Red Teaming & Penetration Testing for LLMs is a carefully structured course is designed for security professionals, AI developers, and ethical hackers aiming to secure generative AI applications. From foundational concepts in LLM security to advanced red teaming techniques, this course equips you with both the knowledge and actionable skills to protect LLM systems.

Throughout the course, you'll engage with practical case studies and attack simulations, including demonstrations on prompt injection, sensitive data disclosure, hallucination handling, model denial of service, and insecure plugin behavior. You'll also learn to use tools, processes, and frameworks like MITRE ATT&CK to assess AI application risks in a structured manner.

By the end of this course, you will be able to identify and exploit vulnerabilities in LLMs, and design mitigation and reporting strategies that align with industry standards.

Key Benefits for You:

LLM Security Insights:
Understand the vulnerabilities of generative AI models and learn proactive testing techniques to identify them.
Penetration Testing Essentials:
Master red teaming strategies, the phases of exploitation, and post-exploitation handling tailored for LLM-based applications.
Hands-On Demos:
Gain practical experience through real-world attack simulations, including biased output, overreliance, and information leaks.
Framework Mastery:
Learn to apply MITRE ATT&CK concepts with hands-on exercises that address LLM-specific threats.
Secure AI Development:
Enhance your skills in building resilient generative AI applications by implementing defense mechanisms like secure output handling and plugin protections.

Join us today for an exciting journey into the world of AI security—enroll now and take the first step towards becoming an expert in LLM penetration testing!

Who this course is for:

SOC Analysts, Security Engineers, and Security Architects aiming to secure LLM systems
CISO, Security Consultants, and AI Security Consultants seeking to protect AI-driven applications.
Red Team/Blue Team members and Penetration Testers exploring LLM exploitation and defense techniques.
Students and tech enthusiasts looking to gain hands-on experience in LLM penetration testing and red teaming.
Ethical Hackers and Incident Handlers wanting to develop skills in securing generative AI models.
Prompt Engineers and Machine Learning Engineers interested in securing AI models and understanding vulnerabilities in LLM-based applications.

Pentesting GenAI LLM models: Securing Large Language Models

What you'll learn

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Course content

Introduction1 lecture • 2min

Introduction to LLM Security & Penetration Testing3 lectures • 10min

Overview of LLM Vulnerabilities13 lectures • 30min

Penetration Testing & Red Teaming Fundamentals5 lectures • 19min

Red Teaming for LLMs1 lecture • 13min

Reporting & Mitigation Strategies2 lectures • 5min

MITRE ATT&CK Framework for LLMs14 lectures • 1hr

LLM Application Vulnerabilities16 lectures • 1hr 2min

Conclusion2 lectures • 2min

Requirements

Description

Who this course is for: