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Static detection of API call vulnerabilities in iOS executables

Published: 27 May 2018 Publication History

Abstract

We propose a static analysis technique for iOS executables for checking API call vulnerabilities that can cause 1) app behaviors to be altered by malicious external inputs, and 2) sensitive user data to be illegally accessed by apps with stealthy private API calls that use string obfuscation. We identify sensitive functions that dynamically load classes/frameworks, and, for each parameter that corresponds to a dynamically loaded class/framework, we construct a dependency graph that shows the set of values that flow to that parameter. A sensitive function that has its class name or framework path parameter depending on external inputs is considered to contain a vulnerability. We further conduct string analysis on these dependency graphs to determine all potential string values that these parameters can take, which identifies the set of dynamically loaded classes/frameworks. Taking the intersection of these values with patterns that characterize Apple's API policies (such as restricted use of private/sensitive APIs), we are able to detect potential policy violations and vulnerabilities.

References

[1]
Paulo Barros, Rene Just, Suzanne Millstein, Paul Vines, Werner Dietl, Marcelo dAmorim, and Michael D. Ernst. 2015. Static Analysis of Implicit Control Flow: Resolving Java Reflection and Android Intents (T). In ASE. 669--679.
[2]
Zhui Deng, Brendan Saltaformaggio, Xiangyu Zhang, and Dongyan Xu. 2015. iris: Vetting private api abuse in ios applications. In CCS. ACM, 44--56.
[3]
Manuel Egele, Christopher Kruegel, Engin Kirda, and Giovanni Vigna. 2011. PiOS: Detecting Privacy Leaks in iOS Applications. In NDSS.
[4]
Li Li, Tegawendé F Bissyandé, Damien Octeau, and Jacques Klein. 2016. DroidRA: taming reflection to support whole-program analysis of Android apps. In ISSTA. 318--329.
[5]
Tim Werthmann, Ralf Hund, Lucas Davi, Ahmad-Reza Sadeghi, and Thorsten Holz. 2013. PSiOS: bring your own privacy & security to iOS devices. In ASIACCS. ACM, 13--24.
[6]
Fang Yu, Yuan-Chieh Lee, Steven Tai, and Wei-Shao Tang. 2013. AppBeach: Characterizing App Behaviors via Static Binary Analysis. In Mobile Services. IEEE Computer Society, 86.

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  1. Static detection of API call vulnerabilities in iOS executables

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    cover image ACM Conferences
    ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings
    May 2018
    231 pages
    ISBN:9781450356633
    DOI:10.1145/3183440
    • Conference Chair:
    • Michel Chaudron,
    • General Chair:
    • Ivica Crnkovic,
    • Program Chairs:
    • Marsha Chechik,
    • Mark Harman
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 27 May 2018

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    Author Tags

    1. API call vulnerability
    2. iOS mobile application
    3. string analysis

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    Overall Acceptance Rate 276 of 1,856 submissions, 15%

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