Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems
Published in arXiv preprint arXiv:2307.01292, 2023
With the emergence of large foundational models, model-serving systems are becoming popular. In such a system, users send the queries to the server and specify the desired performance metrics (e.g., accuracy, latency, etc.). The server maintains a set of models (model zoo) in the back-end and serves the queries based on the specified metrics. This paper examines the security, specifically robustness against model extraction attacks, of such systems. We propose a query-efficient fingerprinting algorithm to enable the attacker to trigger any desired model consistently. We show that by using our fingerprinting algorithm, model extraction can have fidelity and accuracy scores within 1% of the scores obtained if attacking in a single-model setting and up to 14.6% gain in accuracy and up to 7.7% gain in fidelity compared to the naive attack…..
Recommended citation: arXiv:2307.01292 https://arxiv.org/abs/2307.01292