Developers use cryptographic APIs in Android with the intent of securing data such as passwords and personal information on mobile devices. In this paper, we ask whether developers use the cryptographic APIs in a fashion that provides typical cryptographic notions of security, e.g., IND-CPA security. We develop program analysis techniques to automatically check programs on the Google Play marketplace, and find that 10,327 out of 11,748 applications that use cryptographic APIs -88% overall -make at least one mistake. These numbers show that applications do not use cryptographic APIs in a fashion that maximizes overall security. We then suggest specific remediations based on our analysis towards improving overall cryptographic security in Android applications.

Abstract-The design of the Android system allows applications to load additional code from external sources at runtime. On the one hand, malware can use this capability to add malicious functionality after it has been inspected by an application store or anti-virus engine at installation time. On the other hand, developers of benign applications can inadvertently introduce vulnerabilities. In this paper, we systematically analyze the security implications of the ability to load additional code in Android. We developed a static analysis tool to automatically detect attempts to load external code using static analysis techniques, and we performed a large-scale study of 1,632 popular applications from the Google Play store, showing that loading external code in an insecure way is a problem in as much as 9.25% of those applications and even 16% of the top 50 free applications. We also show how malware can use code-loading techniques to avoid detection by exploiting a conceptual weakness in current Android malware protection. Finally, we propose modifications to the Android framework that enforce integrity checks on code to mitigate the threats imposed by the ability to load external code.

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Over the last two years, the Rowhammer bug transformed from a hard-to-exploit DRAM disturbance error into a fully weaponized attack vector. Researchers demonstrated exploits not only against desktop computers, but also used single bit flips to compromise the cloud and mobile devices, all without relying on any software vulnerability. Since hardware-level mitigations cannot be backported, a search for software defenses is pressing. Proposals made by both academia and industry, however, are either impractical to deploy, or insufficient in stopping all attacks: we present rampage, a set of DMA-based Rowhammer attacks against the latest Android OS, consisting of (1) a root exploit, and (2) a series of app-to-app exploit scenarios that bypass all defenses. To mitigate Rowhammer exploitation on ARM, we propose guardion, a lightweight defense that prevents DMA-based attacks-the main attack vector on mobile devices-by isolating DMA buffers with guard rows. We evaluate guardion on 22 benchmark apps and show that it has a negligible memory overhead (2.2 MB on average). We further show that we can improve system performance by re-enabling higher order allocations after Google disabled these as a reaction to previous attacks.

Mobile apps are notorious for collecting a wealth of private information from users. Despite significant effort from the research community in developing privacy leak detection tools based on data flow tracking inside the app or through network traffic analysis, it is still unclear whether apps and ad libraries can hide the fact that they are leaking private information. In fact, all existing analysis tools have limitations: data flow tracking suffers from imprecisions that cause false positives, as well as false negatives when the data flow from a source of private information to a network sink is interrupted; on the other hand, network traffic analysis cannot handle encryption or custom encoding.We propose a new approach to privacy leak detection that is not affected by such limitations, and it is also resilient to obfuscation techniques, such as encoding, formatting, encryption, or any other kind of transformation performed on private information before it is leaked. Our work is based on blackbox differential analysis, and it works in two steps: first, it establishes a baseline of the network behavior of an app; then, it modifies sources of private information, such as the device ID and location, and detects leaks by observing deviations in the resulting network traffic. The basic concept of black-box differential analysis is not novel, but, unfortunately, it is not practical enough to precisely analyze modern mobile apps. In fact, their network traffic contains many sources of non-determinism, such as random identifiers, timestamps, and server-assigned session identifiers, which, when not handled properly, cause too much noise to correlate output changes with input changes.The main contribution of this work is to make black-box differential analysis practical when applied to modern Android apps. In particular, we show that the network-based non-determinism can often be explained and eliminated, and it is thus possible to reliably use variations in the network traffic as a strong signal to detect privacy leaks. We implemented this approach in a tool, called AGRIGENTO, and we evaluated it on more than one thousand Android apps. Our evaluation shows that our approach works well in practice and outperforms current state-of-the-art techniques. We conclude our study by discussing several case studies that show how popular apps and ad libraries currently exfiltrate data by using complex combinations of encoding and encryption mechanisms that other approaches fail to detect. Our results show that these apps and libraries seem to deliberately hide their data leaks from current approaches and clearly demonstrate the need for an obfuscation-resilient approach such as ours.Permission to freely reproduce all or part of this paper for noncommercial purposes is granted provided that copies bear this notice and the full citation on the first page. Reproduction for commercial purposes is strictly prohibited without the prior written consent of the Internet Society, the first-named author (for reproduction of an entire paper o...

For the past two decades, the security community has been fighting malicious programs for Windows-based operating systems. However, the recent surge in adoption of embedded devices and the IoT revolution are rapidly changing the malware landscape. Embedded devices are profoundly different than traditional personal computers. In fact, while personal computers run predominantly on x86-flavored architectures, embedded systems rely on a variety of different architectures. In turn, this aspect causes a large number of these systems to run some variants of the Linux operating system, pushing malicious actors to give birth to "Linux malware."To the best of our knowledge, there is currently no comprehensive study attempting to characterize, analyze, and understand Linux malware. The majority of resources on the topic are available as sparse reports often published as blog posts, while the few systematic studies focused on the analysis of specific families of malware (e.g., the Mirai botnet) mainly by looking at their network-level behavior, thus leaving the main challenges of analyzing Linux malware unaddressed.This work constitutes the first step towards filling this gap. After a systematic exploration of the challenges involved in the process, we present the design and implementation details of the first malware analysis pipeline specifically tailored for Linux malware. We then present the results of the first largescale measurement study conducted on 10,548 malware samples (collected over a time frame of one year) documenting detailed statistics and insights that can help directing future work in the area.

A wealth of recent research proposes static data flow analysis for the security analysis of Android applications. One of the building blocks that these analysis systems rely upon is the computation of a precise control flow graph. The callback mechanism provided and orchestrated by the Android framework makes the correct generation of the control flow graph a challenging endeavor. From the analysis' point of view, the invocation of a callback is an implicit control flow transition facilitated by the framework. Existing static analysis tools model callbacks either through manually-curated lists or ad-hoc heuristics. This work demonstrates that both approaches are insufficient, and allow malicious applications to evade detection by state-of-theart analysis systems. To address the challenge of implicit control flow transitions (i.e., callbacks) through the Android framework, we are the first to propose, implement, and evaluate a systematic treatment of this aspect. Our implementation, called EDGEMINER, statically analyzes the entire Android framework to automatically generate API summaries that describe implicit control flow transitions through the Android framework. We use EDGEMINER to analyze three major versions of the Android framework. EDGEMINER identified 19,647 callbacks in Android 4.2, suggesting that a manual treatment of this challenge is likely infeasible. Our evaluation demonstrates that the current insufficient treatment of callbacks in state-of-the-art analysis tools results in unnecessary imprecision. For example, FlowDroid misses a variety of leaks of privacy sensitive data from benign off-the-shelf Android applications because of its inaccurate handling of callbacks. Of course, malicious applications can also leverage this blind spot in current analysis systems to evade detection at will. The results of our work allow existing tools to comprehensively address the challenge of callbacks and identify previously undetected leakage of privacy sensitive data.