Introduction to LLM Vulnerabilities
Master LLM security through hands-on training in detecting and preventing risks like model theft, prompt injection & data breaches using proven safeguards.
Master LLM security through hands-on training in detecting and preventing risks like model theft, prompt injection & data breaches using proven safeguards.
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
Instructor
20 Courses
A Technology Educator and Former Olympic Athlete Pioneering AI Innovation
Alfredo Deza embodies a unique combination of athletic excellence and technological expertise, transitioning from a distinguished career as Peru's first World Junior Champion in high jump and 2004 Olympian to becoming a leading voice in technology education and development. Currently serving as a Principal Cloud Advocate at Microsoft and Adjunct Assistant Professor at Duke University's Pratt School of Engineering, Deza has built an impressive career spanning nearly two decades in software engineering and education. His academic contributions extend through guest lectures at prestigious institutions including Oxford University, Georgia Tech, and Carnegie Mellon University, where he shares expertise in machine learning, cloud computing, and programming languages. As an accomplished author, he has co-authored several influential books with O'Reilly Media, including "Practical MLOps" and "Python for DevOps," while developing comprehensive courses on Coursera covering topics from large language models to Rust programming. His teaching portfolio at Duke includes graduate-level courses in machine learning operations and Python programming, reflecting his commitment to making complex technical concepts accessible. Deza's expertise encompasses a broad spectrum of technologies, including Azure, MLOps, DevOps, Python, Rust, and Databricks, which he leverages to bridge the gap between academic theory and industry practice. His unique perspective, shaped by his background as an Olympic athlete, influences his approach to teaching and technology, emphasizing the importance of continuous learning and knowledge sharing in the rapidly evolving field of artificial intelligence and cloud computing.
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Frequently asked questions
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