Date of Defense
12-11-2024 1:30 PM
Location
E1-1012
Document Type
Thesis Defense
Degree Name
Master of Science in Information Security
College
CIT
Department
Information Security
First Advisor
Prof. Mohammad Mehedy Masud
Keywords
Large Language Models, NLP, Security, Risk Rating, OWASP, fine-tuning, defense mechanisms
Abstract
This thesis examines the use of Large Language Models (LLMs) in education, with a focus on improving performance and implementing strong security measures. The research has two main goals, namely, the development of an effective lecture summarization technique using LLMs and identifying and addressing security vulnerabilities in LLM applications according to OWASP (Open Web Application Security Project) guidelines. For the former goal, we have proposed an effective framework for fine-tuning LLMs using real lecture datasets and compared the performance of different LLMs. For the latter goal, we conducted a thorough review of the application dataflow of the proposed framework and revealed several vulnerabilities, categorized as high risk, medium risk, and low risk. We also propose countermeasures to these vulnerabilities and demonstrate their efficacy. Thus, this study suggests a framework for securely integrating LLMs into educational purposes, tackling critical security concerns while harnessing the models' efficiency.
Included in
A SECURE AND EFFECTIVE FRAMEWORK FOR KEY CONCEPT MINING FROM EDUCATIONAL CONTENT USING LARGE LANGUAGE MODELS
E1-1012
This thesis examines the use of Large Language Models (LLMs) in education, with a focus on improving performance and implementing strong security measures. The research has two main goals, namely, the development of an effective lecture summarization technique using LLMs and identifying and addressing security vulnerabilities in LLM applications according to OWASP (Open Web Application Security Project) guidelines. For the former goal, we have proposed an effective framework for fine-tuning LLMs using real lecture datasets and compared the performance of different LLMs. For the latter goal, we conducted a thorough review of the application dataflow of the proposed framework and revealed several vulnerabilities, categorized as high risk, medium risk, and low risk. We also propose countermeasures to these vulnerabilities and demonstrate their efficacy. Thus, this study suggests a framework for securely integrating LLMs into educational purposes, tackling critical security concerns while harnessing the models' efficiency.