22 research outputs found
Ghera: A Repository of Android App Vulnerability Benchmarks
Security of mobile apps affects the security of their users. This has fueled
the development of techniques to automatically detect vulnerabilities in mobile
apps and help developers secure their apps; specifically, in the context of
Android platform due to openness and ubiquitousness of the platform. Despite a
slew of research efforts in this space, there is no comprehensive repository of
up-to-date and lean benchmarks that contain most of the known Android app
vulnerabilities and, consequently, can be used to rigorously evaluate both
existing and new vulnerability detection techniques and help developers learn
about Android app vulnerabilities. In this paper, we describe Ghera, an open
source repository of benchmarks that capture 25 known vulnerabilities in
Android apps (as pairs of exploited/benign and exploiting/malicious apps). We
also present desirable characteristics of vulnerability benchmarks and
repositories that we uncovered while creating Ghera.Comment: 10 pages. Accepted at PROMISE'1
Boosting Static Analysis of Android Apps through Code Instrumentation
Static analysis has been applied to dissect Android apps for many years. The main advantage of using static analysis is its efficiency and entire code coverage characteristics. However, the community has not yet produced complete tools to perform in-depth static analysis, putting users at risk to malicious apps. Because of the diverse challenges caused by Android apps, it is hard for a single tool to efficiently address all of them. Thus, in this work, we propose to boost static analysis of Android apps through code instrumentation, in which the knotty code can be reduced or simplified into an equivalent but analyzable code. Consequently, existing static analyzers, without any modification, can be leveraged to perform extensive analysis, although originally they cannot.
Previously, we have successfully applied instrumentation for two challenges of static analysis of Android apps: Inter-Component Communication (ICC) and Reflection. However, these two case studies are implemented separately and the implementation is not reusable, letting some functionality, that could be reused from one to another, be reinvented and thus lots of resources are wasted. To this end, in this work, we aim at providing a generic and non-invasive approach for existing static analyzers, enabling them to perform more broad analysis
I know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis
Android applications may leak privacy data carelessly or maliciously. In this
work we perform inter-component data-flow analysis to detect privacy leaks
between components of Android applications. Unlike all current approaches, our
tool, called IccTA, propagates the context between the components, which
improves the precision of the analysis. IccTA outperforms all other available
tools by reaching a precision of 95.0% and a recall of 82.6% on DroidBench. Our
approach detects 147 inter-component based privacy leaks in 14 applications in
a set of 3000 real-world applications with a precision of 88.4%. With the help
of ApkCombiner, our approach is able to detect inter-app based privacy leaks
Neural-Augmented Static Analysis of Android Communication
We address the problem of discovering communication links between
applications in the popular Android mobile operating system, an important
problem for security and privacy in Android. Any scalable static analysis in
this complex setting is bound to produce an excessive amount of
false-positives, rendering it impractical. To improve precision, we propose to
augment static analysis with a trained neural-network model that estimates the
probability that a communication link truly exists. We describe a
neural-network architecture that encodes abstractions of communicating objects
in two applications and estimates the probability with which a link indeed
exists. At the heart of our architecture are type-directed encoders (TDE), a
general framework for elegantly constructing encoders of a compound data type
by recursively composing encoders for its constituent types. We evaluate our
approach on a large corpus of Android applications, and demonstrate that it
achieves very high accuracy. Further, we conduct thorough interpretability
studies to understand the internals of the learned neural networks.Comment: Appears in Proceedings of the 2018 ACM Joint European Software
Engineering Conference and Symposium on the Foundations of Software
Engineering (ESEC/FSE
Borrowing your enemy's arrows: the case of code reuse in android via direct inter-app code invocation
{The Android ecosystem offers different facilities to enable communication among app components and across apps to ensure that rich services can be composed through functionality reuse. At the heart of this system is the Inter-component communication (ICC) scheme, which has been largely studied in the literature. Less known in the community is another powerful mechanism that allows for direct inter-app code invocation which opens up for different reuse scenarios, both legitimate or malicious. This paper exposes the general workflow for this mechanism, which beyond ICCs, enables app developers to access and invoke functionalities (either entire Java classes, methods or object fields) implemented in other apps using official Android APIs. We experimentally showcase how this reuse mechanism can be leveraged to â plagiarize" supposedly-protected functionalities. Typically, we were able to leverage this mechanism to bypass security guards that a popular video broadcaster has placed for preventing access to its video database from outside its provided app. We further contribute with a static analysis toolkit, named DICIDer, for detecting direct inter-app code invocations in apps. An empirical analysis of the usage prevalence of this reuse mechanism is then conducted. Finally, we discuss the usage contexts as well as the implications of this studied reuse mechanis
A Study of Android Application Security.
Abstract The fluidity of application markets complicate smartphone security. Although recent efforts have shed light on particular security issues, there remains little insight into broader security characteristics of smartphone applications. This paper seeks to better understand smartphone application security by studying 1,100 popular free Android applications. We introduce the ded decompiler, which recovers Android application source code directly from its installation image. We design and execute a horizontal study of smartphone applications based on static analysis of 21 million lines of recovered code. Our analysis uncovered pervasive use/misuse of personal/phone identifiers, and deep penetration of advertising and analytics networks. However, we did not find evidence of malware or exploitable vulnerabilities in the studied applications. We conclude by considering the implications of these preliminary findings and offer directions for future analysis