Automatically Securing Permission-Based Software by Reducing the Attack Surface: An Application to Android

A common security architecture, called the permission-based security model (used e.g. in Android and Blackberry), entails intrinsic risks. For instance, applications can be granted more permissions than they actually need, what we call a "permission gap". Malware can leverage the unused permissions for achieving their malicious goals, for instance using code injection. In this paper, we present an approach to detecting permission gaps using static analysis. Our prototype implementation in the context of Android shows that the static analysis must take into account a significant amount of platform-specific knowledge. Using our tool on two datasets of Android applications, we found out that a non negligible part of applications suffers from permission gaps, i.e. does not use all the permissions they declare

Static Analysis for Extracting Permission Checks of a Large Scale Framework: The Challenges And Solutions for Analyzing Android

A common security architecture is based on the protection of certain resources by permission checks (used e.g., in Android and Blackberry). It has some limitations, for instance, when applications are granted more permissions than they actually need, which facilitates all kinds of malicious usage (e.g., through code injection). The analysis of permission-based framework requires a precise mapping between API methods of the framework and the permissions they require. In this paper, we show that naive static analysis fails miserably when applied with off-the-shelf components on the Android framework. We then present an advanced class-hierarchy and field-sensitive set of analyses to extract this mapping. Those static analyses are capable of analyzing the Android framework. They use novel domain specific optimizations dedicated to Android.Comment: IEEE Transactions on Software Engineering (2014). arXiv admin note: substantial text overlap with arXiv:1206.582

In-Vivo Bytecode Instrumentation for Improving Privacy on Android Smartphones in Uncertain Environments

In this paper we claim that an efficient and readily applicable means to improve privacy of Android applications is: 1) to perform runtime monitoring by instrumenting the application bytecode and 2) in-vivo, i.e. directly on the smartphone. We present a tool chain to do this and present experimental results showing that this tool chain can run on smartphones in a reasonable amount of time and with a realistic effort. Our findings also identify challenges to be addressed before running powerful runtime monitoring and instrumentations directly on smartphones. We implemented two use-cases leveraging the tool chain: BetterPermissions, a fine-grained user centric permission policy system and AdRemover an advertisement remover. Both prototypes improve the privacy of Android systems thanks to in-vivo bytecode instrumentation.Comment: ISBN: 978-2-87971-111-

MUSTI: Dynamic Prevention of Invalid Object Initialization Attacks

Invalid object initialization vulnerabilities have been identified since the 1990’s by a research group at Princeton University. These vulnerabilities are critical since they can be used to totally compromise the security of a Java virtual machine.Recently, such a vulnerability identified as CVE-2017-3289 has been found again in the bytecode verifier of the JVM and affects more than 40 versions of the JVM. In this paper, we present a runtime solution called MUSTIto detect and prevent attacks leveraging this kind of critical vulnerabilities. We optimize MUSTI to have a runtime overhead below 0.5% and a memory overhead below 0.42%. Compared to state-of-the-art, MUSTI is completely automated and does not require to manually annotate the code