Cloud backends provide essential features to the mobile app ecosystem, such as content delivery, ad networks, analytics, and more. Unfortunately, app developers often disregard or have no control over prudent security practices when choosing or managing these services. Our preliminary study of the top 5,000 Google Play Store free apps identified 983 instances of N-day and 655 instances of 0-day vulnerabilities spanning across the software layers (OS, software services, communication, and web apps) of cloud backends. The mobile apps using these cloud backends represent between 1M and 500M installs each and can potentially affect hundreds of thousands of users. Further, due to the widespread use of third-party SDKs, app developers are often unaware of the backends affecting their apps and where to report vulnerabilities. This paper presents SkyWalker, a pipeline to automatically vet the backends that mobile apps contact and provide actionable remediation. For an input APK, SkyWalker extracts an enumeration of backend URLs, uses remote vetting techniques to identify software vulnerabilities and responsible parties, and reports mitigation strategies to the app developer. Our findings suggest that developers and cloud providers do not have a clear understanding of responsibilities and liabilities in regards to mobile app backends that leave many vulnerabilities exposed.
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Automatic Uncovering of Hidden Behaviors from Input Validation in Mobile Apps
Mobile applications (apps) have exploded in popularity, with billions of smartphone users using millions of apps available through markets such as the Google Play Store or the Apple App Store. While these apps have rich and useful functionality that is publicly exposed to end users, they also contain hidden behaviors that are not disclosed, such as backdoors and blacklists designed to block unwanted content. In this paper, we show that the input validation behavior---the way the mobile apps process and respond to data entered by users---can serve as a powerful tool for uncovering such hidden functionality. We therefore have developed a tool, InputScope, that automatically detects both the execution context of user input validation and also the content involved in the validation, to automatically expose the secrets of interest. We have tested InputScope with over 150,000 mobile apps, including popular apps from major app stores and pre-installed apps shipped with the phone, and found 12,706 mobile apps with backdoor secrets and 4,028 mobile apps containing blacklist secrets.
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- Award ID(s):
- 1657199
- PAR ID:
- 10172957
- Date Published:
- Journal Name:
- Proceedings of the IEEE Symposium on Security and Privacy
- Volume:
- 1
- ISSN:
- 2375-1207
- Page Range / eLocation ID:
- 1106-1120
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SP40000.2020.00072
