Security analysis of permission re-delegation vulnerabilities in Android apps

• Published in: Empirical software engineering : an international journal Vol. 25; no. 6; pp. 5084 - 5136
• Main Authors: Demissie, Biniam Fisseha, Ceccato, Mariano, Shar, Lwin Khin
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.11.2020; Springer Nature B.V
• Subjects: Applications programs; Clusters; Compilers; Computer Science; End users; Genetic algorithms; Interpreters; Machine learning; Mobile operating systems; Natural language processing; Programming Languages; Security; Software Engineering/Programming and Operating Systems; Speech recognition; Permission re-delegation; Genetic algorithm; Outlier detection; Natural language processing; Program analysis; Android; Test generation
• ISSN: 1382-3256; 1573-7616
• DOI: 10.1007/s10664-020-09879-8

The Android platform facilitates reuse of app functionalities by allowing an app to request an action from another app through inter-process communication mechanism. This feature is one of the reasons for the popularity of Android, but it also poses security risks to the end users because malicious, unprivileged apps could exploit this feature to make privileged apps perform privileged actions on behalf of them. In this paper, we investigate the hybrid use of program analysis, genetic algorithm based test generation, natural language processing, machine learning techniques for precise detection of permission re-delegation vulnerabilities in Android apps. Our approach first groups a large set of benign and non-vulnerable apps into different clusters, based on their similarities in terms of functional descriptions. It then generates permission re-delegation model for each cluster, which characterizes common permission re-delegation behaviors of the apps in the cluster. Given an app under test, our approach checks whether it has permission re-delegation behaviors that deviate from the model of the cluster it belongs to. If that is the case, it generates test cases to detect the vulnerabilities. We evaluated the vulnerability detection capability of our approach based on 1,258 official apps and 20 mutated apps. Our approach achieved 81.8% recall and 100% precision. We also compared our approach with two static analysis-based approaches — Covert and IccTA — based on 595 open source apps. Our approach detected 30 vulnerable apps whereas Covert detected one of them and IccTA did not detect any. Executable proof-of-concept attacks generated by our approach were reported to the corresponding app developers.