First Advisor
Wu-chang Feng
Date of Award
Fall 12-2025
Document Type
Thesis
Degree Name
Bachelor of Science (B.S.) in Computer Science and University Honors
Department
Computer Science
Language
English
Subjects
LLM, GenAI, cybersecurity, artificial intelligence, large language model, vulnerabilities
Abstract
Generative AI (GenAI) applications such as OpenAI’s ChatGPT leverage large language models (LLMs) trained on enormous amounts of data to accomplish tasks such as document editing, summarization, and query response. Chatbots and LLM programs that are equipped with retrieval-augmented generation (RAG) have the ability to draw upon data provided by developers and users to improve the quality of the program’s responses. LLM technology has even expanded to generate images, audio, and video from user instructions. Designed around unpredictable user input and typically composed of many opaque components, LLM software products face a paradigm shift of new, constantly evolving security challenges. This paper overviews these emerging challenges and presents best security practices for building and using generative AI applications.
Recommended Citation
Klein, Kyle, "Security Vulnerabilities and Defense Tactics for Generative AI Application Development" (2025). University Honors Theses. Paper 1716.