Android Repository Mining for Detecting Publicly Accessible Functions Missing Permission Checks

Nguyen, Hoang H.; Jiang, Lingxiao; Tho, Quan Thanh

doi:10.1109/icpc.2017.14

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…However, as discussed in Section 2.3, there are no readily available mappings between Android APIs and dangerous permissions in the Android documentation [2]. Although existing studies (e.g., [1,18,[58][59][60][61]) proposed several techniques to infer such mappings, we found that their released datasets are outdated. To precisely locate issues of Type 2, we chose to infer the mappings by ourselves.…”

Section: Keyword Formulationmentioning

confidence: 95%

“…Existing studies [3,58,59,61,[82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101] have investigated many aspects of Android permission system. We discuss those that are most relevant to our work below.…”

Section: Related Workmentioning

confidence: 99%

“…Inferring API-permission mappings. Several approaches [6,18,30,31,[58][59][60][61] were proposed to infer the mappings between permissions and Android APIs via performing static analysis on Android framework. Felt et al [1] proposed a technique Stowaway to construct permission specification, which used test generation to exercise Android APIs, and modified the internal permission check mechanism to log all permission checks on Android 2.2.…”

Section: Related Workmentioning

confidence: 99%

“…They conducted the experiments on a large set of apps to detect permission gap issues (for Android 2.2), i.e., there are more permissions than necessary. Nguyen et al [61] presented an approach to identifying Android APIs that access sensitive system or user data. They analyzed six versions of Android (4.1.1-6.0.1) and detected the APIs that may be sufficiently protected by permission checks.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Runtime Permission Issues in Android Apps: Taxonomy, Practices, and Ways Forward

Wang¹,

Wang²,

Wang³

et al. 2021

Preprint

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Android introduces a new permission model that allows apps to request permissions at runtime rather than at the installation time since 6.0 (Marshmallow, API level 23). While this runtime permission model provides users with greater flexibility in controlling an app's access to sensitive data and system features, it brings new challenges to app development. First, as users may grant or revoke permissions at any time while they are using an app, developers need to ensure that the app properly checks and requests required permissions before invoking any permission-protected APIs. Second, Android's permission mechanism keeps evolving and getting customized by device manufacturers. Developers are expected to comprehensively test their apps on different Android versions and device models to make sure permissions are properly requested in all situations. Unfortunately, these requirements are often impractical for developers. In practice, many Android apps suffer from various runtime permission issues (ARP issues). While existing studies have explored ARP issues, the understanding of such issues is still preliminary. To better characterize ARP issues, we performed an empirical study using 135 Stack Overflow posts that discuss ARP issues and 199 real ARP issues archived in popular open-source Android projects on GitHub. Via analyzing the data, we observed 11 types of ARP issues that commonly occur in Android apps. For each type of issues, we systematically studied: (1) how they can be manifested, (2) how pervasive and serious they are in real-world apps, and (3) how they can be fixed. We also analyzed the evolution trend of different types of issues from 2015 to 2020 to understand their impact on the Android ecosystem. Furthermore, we conducted a field survey and in-depth interviews among practitioners, to gain insights from industrial practices and learn practitioners' requirements of tools that can help combat ARP issues. Finally, to understand the strengths and weaknesses of the existing ARP issue detectors, we built ARPBENCH, an open benchmark consisting of 94 real ARP issues, and evaluated the performance of three available ARP issue detectors. The experimental results indicate that the existing tools have very limited supports for detecting our observed issue types and report a large number of false alarms. We further analyzed the tools' limitations and summarized the challenges of designing an effective ARP issue detection technique. We hope that our findings can shed light on future research and provide useful guidance to practitioners.

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