Budget and Frequency Controlled Cost-Aware Model Extraction Attack on Sequential Recommenders

Lei Zhou, Min Gao, Zongwei Wang, Yibing Bai

Abstract: Sequential recommenders are integral to many applications yet remain vulnerable to model extraction attacks, in which adversaries can recover information about the deployed model by issuing queries to a black-box without internal access. From the attacker's perspective, existing studies impose a fixed and limited query budget but overlook optimal allocation, resulting in redundant or low-value requests. Furthermore, the scarce data obtained through these costly queries is typically handled by crude random sampling, resulting in low diversity and information coverage with actual data. In this paper, we propose a novel approach, named Budget and Frequency Controlled Cost-Aware Model Extraction Attack (BECOME), for extracting black-box sequential recommenders, which extends the standard extraction framework with two cost-aware innovations: Feedback-Driven Dynamic Budgeting periodically evaluates the victim model to refine query allocation and steer sequence generation adaptively. Rank-Aware Frequency Controlling integrates frequency constraints with ranking guidance in the next-item sampler to select high-value items and broaden information coverage. Experiments on public datasets and representative sequential recommender architectures demonstrate that our method achieves superior extraction performance. Our code is released at https://github.com/Loche2/BECOME.

External IDs:doi:10.1145/3746252.3761032