Invetter

Abstract

Android integrates an increasing number of features into system services to manage sensitive resources, such as location, medical and social network information. To prevent untrusted apps from abusing the services, Android implements a comprehensive set of access controls to ensure proper usage of sensitive resources. Unlike explicit permission-based access controls that are discussed extensively in the past, our paper focuses on the widespread yet undocumented input validation problem. As we show in the paper, there are in fact more input validations acting as security checks than permission checks, rendering them a critical foundation for Android framework. Unfortunately, these validations are unstructured, ill-defined, and fragmented, making it challenging to analyze. To this end, we design and implement a tool, called Invetter, that combines machine learning and static analysis to locate sensitive input validations that are problematic in system services. By applying Invetter to 4 different AOSP codebases and 4 vendor-customized images, we locate 103 candidate insecure validations. Among the true positives, we are able to confirm that at least 20 of them are truly exploitable vulnerabilities by constructing various attacks such as privilege escalation and private information leakage.