UROP Research Mentor Project Submission Portal: Submission #997

Overall Research Project Description Machine learning models are becoming a key part of many everyday applications—from search engines and virtual assistants to healthcare and banking. However, as these models become more powerful and widely used, they also become more attractive targets for attackers. One serious threat is called a model extraction attack, where an outsider tries to “steal” a trained model by sending queries and analyzing the outputs. This stolen model can then be misused, duplicated, or reverse-engineered, leading to intellectual property theft, loss of competitive advantage, and serious privacy risks.

This research project focuses on understanding how these attacks work and developing effective defenses. We want to study how attackers interact with machine learning services (often offered as APIs) and figure out what information they can extract. Then, we will design and test various protective strategies to make models more resistant—without significantly lowering their accuracy or slowing them down for legitimate users.

This project is a great opportunity for students interested in cybersecurity, artificial intelligence, or the ethical and legal implications of new technologies. No matter your background, if you’re curious about how smart systems can be tricked—and how to make them safer—this research will give you hands-on experience at the frontier of AI security.