Mobile Application Web API Reconnaissance: Web-to-Mobile Inconsistencies & Vulnerabilities
Conference Paper
Overview
Research
Identity
Additional Document Info
Other
View All
Overview
abstract
2018 IEEE. Modern mobile apps use cloud-hosted HTTP-based API services and heavily rely on the Internet infrastructure for data communication and storage. To improve performance and leverage the power of the mobile device, input validation and other business logic required for interfacing with web API services are typically implemented on the mobile client. However, when a web service implementation fails to thoroughly replicate input validation, it gives rise to inconsistencies that could lead to attacks that can compromise user security and privacy. Developing automatic methods of auditing web APIs for security remains challenging. In this paper, we present a novel approach for automatically analyzing mobile app-to-web API communication to detect inconsistencies in input validation logic between apps and their respective web API services. We present our system, WARDroid, which implements a static analysis-based web API reconnaissance approach to uncover inconsistencies on real world API services that can lead to attacks with severe consequences for potentially millions of users throughout the world. Our system utilizes program analysis techniques to automatically extract HTTP communication templates from Android apps that encode the input validation constraints imposed by the apps on outgoing web requests to web API services. WARDroid is also enhanced with blackbox testing of server validation logic to identify inconsistencies that can lead to attacks. We evaluated our system on a set of 10,000 popular free apps from the Google Play Store. We detected problematic logic in APIs used in over 4,000 apps, including 1,743 apps that use unencrypted HTTP communication. We further tested 1,000 apps to validate web API hijacking vulnerabilities that can lead to potential compromise of user privacy and security and found that millions of users are potentially affected from our sample set of tested apps.
