Modern operating systems, such as iOS, use multiple access control policies to define an overall protection system. However, the complexity of these policies and their interactions can hide policy flaws that compromise the security of the protection system. We propose iOracle, a framework that logically models the iOS protection system such that queries can be made to automatically detect policy flaws. iOracle models policies and runtime context extracted from iOS firmware images, developer resources, and jailbroken devices, and iOracle significantly reduces the complexity of queries by modeling policy semantics. We evaluate iOracle by using it to successfully triage executables likely to have policy flaws and comparing our results to the executables exploited in four recent jailbreaks. When applied to iOS 10, iOracle identifies previously unknown policy flaws that allow attackers to modify or bypass access control policies. For compromised system processes, consequences of these policy flaws include sandbox escapes (with respect to read/write file access) and changing the ownership of arbitrary files. By automating the evaluation of iOS access control policies, iOracle provides a practical approach to hardening iOS security by identifying policy flaws before they are exploited.

Recent literature on iOS security has focused on the malicious potential of third-party applications, demonstrating how developers can bypass application vetting and code-level protections. In addition to these protections, iOS uses a generic sandbox profile called "container" to confine malicious or exploited third-party applications. In this paper, we present the first systematic analysis of the iOS container sandbox profile. We propose the SandScout framework to extract, decompile, formally model, and analyze iOS sandbox profiles as logic-based programs. We use our Prolog-based queries to evaluate file-based security properties of the container sandbox profile for iOS 9.0.2 and discover seven classes of exploitable vulnerabilities. These attacks affect non-jailbroken devices running later versions of iOS. We are working with Apple to resolve these attacks, and we expect that SandScout will play a significant role in the development of sandbox profiles for future versions of iOS.

Microblogging is a popular form of online social networking activity. It allows users to send messages in a one-to-many publish-subscribe manner. Most current service providers are centralized and deploy a client-server model with unencrypted message content. As a consequence, all user behavior can, by default, be monitored, and censoring based on message content can easily be enforced on the server side. A distributed, peer-to-peer microblogging system consisting of mobile smartphone-equipped users that exchange group encrypted messages in an anonymous and censorship-resistant manner can alleviate privacy and censorship issues. We experimentally evaluate message spread of such systems with simulations that run on a range of synthetic and real-world mobility inputs, thus extending the previous work. We show that such systems are feasible for a range of mobility and network settings, both under normal and under adversarial conditions, e.g., under the presence of nodes which jam the network or send spam.

Until very recently it was widely believed that iOS malware is effectively blocked by Apple's vetting process and application sandboxing. However, the newly presented severe malicious app attacks (e.g., Jekyll) succeeded to undermine these protection measures and steal private data, post Twitter messages, send SMS, and make phone calls. Currently, no effective defenses against these attacks are known for iOS.