TaintDroid: an information-flow tracking system for realtime privacy monitoring on smartphones
OSDI'10 Proceedings of the 9th USENIX conference on Operating systems design and implementation
Directed incremental symbolic execution
Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation
Mobile Security Catching Up? Revealing the Nuts and Bolts of the Security of Mobile Devices
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
A study of android application security
SEC'11 Proceedings of the 20th USENIX conference on Security
Permission re-delegation: attacks and defenses
SEC'11 Proceedings of the 20th USENIX conference on Security
A survey of mobile malware in the wild
Proceedings of the 1st ACM workshop on Security and privacy in smartphones and mobile devices
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iOS is Apple's mobile operating system, which is used on iPhone, iPad and iPod touch. Any third-party applications developed for iOS devices are required to go through Apple's application vetting process and appear on the official iTunes App Store upon approval. When an application is downloaded from the store and installed on an iOS device, it is given a limited set of privileges, which are enforced by iOS application sandbox. Although details of the vetting process and the sandbox are kept as black box by Apple, it was generally believed that these iOS security mechanisms are effective in defending against malwares. In this paper, we propose a generic attack vector that enables third-party applications to launch attacks on non-jailbroken iOS devices. Following this generic attack mechanism, we are able to construct multiple proof-of-concept attacks, such as cracking device PIN and taking snapshots without user's awareness. Our applications embedded with the attack codes have passed Apple's vetting process and work as intended on non-jailbroken devices. Our proof-of-concept attacks have shown that Apple's vetting process and iOS sandbox have weaknesses which can be exploited by third-party applications. We further provide corresponding mitigation strategies for both vetting and sandbox mechanisms, in order to defend against the proposed attack vector.