This Project Grant award of $500,000 from the National Science Foundation's (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program supports research to understand and mitigate security vulnerabilities in machine learning (ML) models. The research aims to characterize how malicious actors could exploit the unused parameters in trained ML models to install covert functionality, and develop mitigation approaches to improve the robustness and trustworthiness of ML systems. Key focus areas include empirically studying the capacity for unused parameters to store data covertly, using information theory and game theory to analyze such threats, and exploring generalizations to emerging ML models like large language models. The research, conducted by the Regents of the University of California at Riverside, a Hispanic-serving research institution, is expected to run from Jun 2025 to May 2028 and will involve training graduate and undergraduate students as well as developing new pedagogical material on ML safety.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $500.0k | 7/11/25 |