Introduction to LLM Vulnerabilities
This course is part of Generative AI Fundamentals.
This comprehensive course explores the critical security challenges presented by large language models (LLMs) and equips learners with essential skills to protect AI systems. Students learn to identify common threats like model theft and prompt injection, implement secure plugin design practices, and establish effective monitoring systems. The curriculum covers techniques for preventing unauthorized access, protecting sensitive information, and maintaining the integrity of LLM applications. Through practical lessons, participants gain expertise in security measures essential for deploying robust AI solutions in today's complex digital landscape.
Instructors:
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What you'll learn
Identify and assess common LLM security vulnerabilities and risks
Implement strategies to prevent model theft and unauthorized access
Design secure plugins and validate input effectively
Protect sensitive information using APIs and regex techniques
Monitor and maintain security through dependency management
Analyze different types of generative AI applications
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments,Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Module Description
The course provides a comprehensive introduction to security considerations in large language model applications. Students learn about various types of vulnerabilities, attack vectors, and mitigation strategies specific to LLMs. The curriculum covers essential topics including model theft prevention, secure plugin design, sensitive information handling, and dependency management. Through theoretical understanding and practical application, participants develop the skills needed to identify and address security challenges in AI systems.
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Generative AI Fundamentals
Instructor
Adjunct Assistant Professor at Duke University
Dr. Alfredo Deza is an Adjunct Assistant Professor in the Pratt School of Engineering at Duke University, where he teaches courses on machine learning, programming, and data engineering. He has been involved in academia for several years, focusing on innovative teaching methods and practical applications of technology. Dr. Deza co-authored the book Practical MLOps and has published several other works related to Python and machine learning. His teaching includes courses such as Python Bootcamp and advanced data engineering topics, and he actively develops online courses available on platforms like Coursera. In addition to his academic role, Dr. Deza works in developer relations at Microsoft, leveraging his extensive experience in software engineering and cloud computing to enhance educational content and support for students and faculty. He collaborates with various universities worldwide, including Georgia Tech and Carnegie Mellon University, to promote knowledge sharing in the field of technology and data science.
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Frequently asked questions
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