S&P2025
Detecting Taint-Style Vulnerabilities in Microservice-Structured Web Applications
Fengyu Liu, Yuan Zhang, Tian Chen, Youkun Shi, Guangliang Yang, Zihan Lin, Min Yang, Junyao He, Qi Li
Abstract
Microservice architecture has been becoming increasingly popular for building scalable and maintainable applications. A microservice-structured web application (shortened to microservice application) enhances security by providing a loose-coupling design and enforcing the security isolation between different microservices. However, in this paper, our study shows microservice applications still suffer from taint-style vulnerability, one of the most serious vulnerabilities. We propose a novel security analysis approach, named MScan, that can effectively detect taint-style vulnerabilities in real-world evolving-fast microservice applications. Our approach mainly consists of three phases. First, MScan identifies the entry points accessible to external malicious users by applying a gateway-centric analysis. Second, MScan utilizes a new data structure, i.e. service dependence graph, to bridge inter-service communication. Finally, MScan employs a distance-guided strategy for selective context-sensitive taint analysis to detect vulnerabilities. By applying MScan on 25 open-source microservice applications and 5 industrial microservice applications from a world-leading fintech company, we found MScan can effectively vet these applications with the discovery of 59 high-risk 0-day vulnerabilities. We have conducted responsible vulnerability disclosure. Up to now, 31 CVE identifiers have been issued.