Exploiting Power Side-Channel Vulnerabilities in XGBoost Accelerator

XGBoost (eXtreme Gradient Boosting), a widelyused decision tree algorithm, plays a crucial role in applications such as ransomware and fraud detection. While its performance is well-established, its security against model extraction on hardware platforms like Field Programmable Gate Arrays (FPGAs) has not been fully explored. In this paper, we demonstrate a significant vulnerability where sensitive model data can be leaked from an XGBoost implementation through side-channel attacks (SCAs). By analyzing variations in power consumption, we show how an attacker can infer node features within the XGBoost model, leading to the extraction of critical data. We conduct an experiment using the XGBoost accelerator FAXID on the Sakura-X platform, demonstrating a method to deduce model decisions by monitoring power consumptions. The results show that on average 367k tests are sufficient to leak sensitive values. Our findings underscore the need for improved hardware and algorithmic protections to safeguard machine learning models from these types of attacks.