BFTDETECTOR: Automatic Detection of Business Flow Tampering for Digital Content Service

I Luk Kim, Weihang Wang, Yonghwi Kwon, Xiangyu Zhang

Abstract: Digital content services provide users with a wide range of content, such as news, articles, or movies, while monetizing their content through various business models and promotional methods. Unfortunately, poorly designed or unpro-tected business logic can be circumvented by malicious users, which is known as business flow tampering. Such flaws can severely harm the businesses of digital content service providers. In this paper, we propose an automated approach that discov-ers business flow tampering flaws. Our technique automatically runs a web service to cover different business flows (e.g., a news website with vs. without a subscription paywall) to collect execution traces. We perform differential analysis on the execution traces to identify divergence points that determine how the business flow begins to differ, and then we test to see if the divergence points can be tampered with. We assess our approach against 352 real-world digital content service providers and discover 315 flaws from 204 websites, including TIME, Fortune, and Forbes. Our evaluation result shows that our technique successfully identifies these flaws with low false-positive and false-negative rates of 0.49% and 1.44%, respectively.