Detecting and Measuring Misconfigured Manifests in Android Apps
Yuqing Yang, Mohamed Elsabagh, Chaoshun Zuo, Ryan Johnson, Angelos Stavrou, Zhiqiang Lin
摘要
The manifest file of an Android app is crucial for app security as it declares sensitive app configurations, such as access permissions required to access app components. Surprisingly, we noticed a number of widely-used apps (some with over 500 million downloads) containing misconfigurations in their manifest files that can result in severe security issues. This paper presents ManiScope, a tool to automatically detect misconfigurations of manifest files when given an Android APK. The key idea is to build a manifest XML Schema by extracting ManiScope constraints from the manifest documentation with novel domain-aware NLP techniques and rules, and validate manifest files against the schema to detect misconfigurations. We have implemented ManiScope, with which we have identified 609,428 (33.20%) misconfigured Android apps out of 1,853,862 apps from Google Play, and 246,658 (35.64%) misconfigured ones out of 692,106 pre-installed apps from 4,580 Samsung firmwares, respectively. Among them, 84,117 (13.80%) of misconfigured Google Play apps and 56,611 (22.95%) of misconfigured pre-installed apps have various security implications including app defrauding, message spoofing, secret data leakage, and component hijacking.
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- Polisis: Automated Analysis and Presentation of Privacy Policies Using Deep LearningHamza Harkous, Kassem Fawaz, Rémi Lebret, Florian Schaub 等USENIX Security 2018 · 被引用 400 次
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