What you'll learn:
- Gain foundational knowledge about Generative AI technologies and their applications.
- Understand the core concepts and methodologies involved in penetration testing for Large Language Models (LLMs).
- Learn the step-by-step process of conducting penetration tests specifically tailored for Generative AI systems.
- Study the MITRE ATT&CK framework and its application in Red Teaming.
- Explore the MITRE ATLAS framework for assessing AI and ML security.
- Review the top 10 vulnerabilities for Large Language Models identified by OWASP.
- Learn about common attacks on Generative AI systems and how to defend against them.
- Dive into a practical case study on exploiting vulnerabilities in a Large Language Model.
Penetration Testing for LLMs is a meticulously structured Udemy course aimed at IT professionals seeking to master Penetration Testing for LLMs for Cybersecurity purposes. This course systematically walks you through the initial basics to advanced concepts with applied case studies.
You will gain a deep understanding of the principles and practices necessary for effective Penetration Testing for LLMs. The course combines theoretical knowledge with practical insights to ensure comprehensive learning. By the end of the course, you'll be equipped with the skills to implement and conduct Penetration Testing for LLMs in your enterprise.
Key Benefits for you:
Basics - Generative AI: Gain a foundational understanding of generative AI, including how it works, its applications, and its security implications.
Penetration Testing: Learn the fundamentals of penetration testing, including methodologies, tools, and techniques for assessing security vulnerabilities.
The Penetration Testing Process for GenAI: Explore a structured approach to penetration testing for generative AI models, focusing on identifying weaknesses and potential exploits.
MITRE ATT&CK: Understand the MITRE ATT&CK framework and how it maps adversarial tactics and techniques used in cyberattacks.
MITRE ATLAS: Learn about MITRE ATLAS, a specialized framework for AI system security, detailing known threats and vulnerabilities in AI applications.
Attacks and Countermeasures for GenAI: Discover common attack vectors targeting generative AI systems and the defensive strategies to mitigate these risks.
Case Study: Exploit a LLM: Analyze a real-world case study demonstrating how adversaries exploit large language models (LLMs) and explore defensive measures.
