Novel Attacks and Defenses in the Userland of Android

In the last decade, mobile devices have spread rapidly, becoming more and more part of our everyday lives; this is due to their feature-richness, mobility, and affordable price. At the time of writing, Android is the leader of the market among operating systems, with a share of 76% and two and a half billion active Android devices around the world. Given that such small devices contain a massive amount of our private and sensitive information, the economic interests in the mobile ecosystem skyrocketed. For this reason, not only legitimate apps running on mobile environments have increased dramatically, but also malicious apps have also been on a steady rise. On the one hand, developers of mobile operating systems learned from security mistakes of the past, and they made significant strides in blocking those threats right from the start. On the other hand, these high-security levels did not deter attackers. In this thesis, I present my research contribution about the most meaningful attack and defense scenarios in the userland of the modern Android operating system. I have emphasized "userland'' because attack and defense solutions presented in this thesis are executing in the userspace of the operating system, due to the fact that Android is slightly different from traditional operating systems. After the necessary technical background, I show my solution, RmPerm, in order to enable Android users to better protect their privacy by selectively removing permissions from any app on any Android version. This operation does not require any modification to the underlying operating system because we repack the original application. Then, using again repackaging, I have developed Obfuscapk; it is a black-box obfuscation tool that can work with every Android app and offers a free solution with advanced state of the art obfuscation techniques -- especially the ones used by malware authors. Subsequently, I present a machine learning-based technique that focuses on the identification of malware in resource-constrained devices such as Android smartphones. This technique has a very low resource footprint and does not rely on resources outside the protected device. Afterward, I show how it is possible to mount a phishing attack -- the historically preferred attack vector -- by exploiting two recent Android features, initially introduced in the name of convenience. Although a technical solution to this problem certainly exists, it is not solvable from a single entity, and there is the need for a push from the entire community. But sometimes, even though there exists a solution to a well-known vulnerability, developers do not take proper precautions. In the end, I discuss the Frame Confusion vulnerability; it is often present in hybrid apps, and it was discovered some years ago, but I show how it is still widespread. I proposed a methodology, implemented in the FCDroid tool, for systematically detecting the Frame Confusion vulnerability in hybrid Android apps. The results of an extensive analysis carried out through FCDroid on a set of the most downloaded apps from the Google Play Store prove that 6.63% (i.e., 1637/24675) of hybrid apps are potentially vulnerable to Frame Confusion. The impact of such results on the Android users' community is estimated in 250.000.000 installations of vulnerable apps

Hidden in Plain Sight: Exploring Encrypted Channels in Android apps

As privacy features in Android operating system improve, privacy-invasive apps may gradually shift their focus to non-standard and covert channels for leaking private user/device information. Such leaks also remain largely undetected by state-of-the-art privacy analysis tools, which are very effective in uncovering privacy exposures via regular HTTP and HTTPS channels. In this study, we design and implement, ThirdEye, to significantly extend the visibility of current privacy analysis tools, in terms of the exposures that happen across various non-standard and covert channels, i.e., via any protocol over TCP/UDP (beyond HTTP/S), and using multi-layer custom encryption over HTTP/S and non-HTTP protocols. Besides network exposures, we also consider covert channels via storage media that also leverage custom encryption layers. Using ThirdEye, we analyzed 12,598 top-apps in various categories from Androidrank, and found that 2887/12,598 (22.92%) apps used custom encryption/decryption for network transmission and storing content in shared device storage, and 2465/2887 (85.38%) of those apps sent device information (e.g., advertising ID, list of installed apps) over the network that can fingerprint users. Besides, 299 apps transmitted insecure encrypted content over HTTP/non-HTTP protocols; 22 apps that used authentication tokens over HTTPS, happen to expose them over insecure (albeit custom encrypted) HTTP/non-HTTP channels. We found non-standard and covert channels with multiple levels of obfuscation (e.g., encrypted data over HTTPS, encryption at nested levels), and the use of vulnerable keys and cryptographic algorithms. Our findings can provide valuable insights into the evolving field of non-standard and covert channels, and help spur new countermeasures against such privacy leakage and security issues.Comment: Extended version of an ACM CCS 2022 pape

“And all the pieces matter...” Hybrid Testing Methods for Android App's Privacy Analysis

Smartphones have become inherent to the every day life of billions of people worldwide, and they are used to perform activities such as gaming, interacting with our peers or working. While extremely useful, smartphone apps also have drawbacks, as they can affect the security and privacy of users. Android devices hold a lot of personal data from users, including their social circles (e.g., contacts), usage patterns (e.g., app usage and visited websites) and their physical location. Like in most software products, Android apps often include third-party code (Software Development Kits or SDKs) to include functionality in the app without the need to develop it in-house. Android apps and third-party components embedded in them are often interested in accessing such data, as the online ecosystem is dominated by data-driven business models and revenue streams like advertising. The research community has developed many methods and techniques for analyzing the privacy and security risks of mobile apps, mostly relying on two techniques: static code analysis and dynamic runtime analysis. Static analysis analyzes the code and other resources of an app to detect potential app behaviors. While this makes static analysis easier to scale, it has other drawbacks such as missing app behaviors when developers obfuscate the app’s code to avoid scrutiny. Furthermore, since static analysis only shows potential app behavior, this needs to be confirmed as it can also report false positives due to dead or legacy code. Dynamic analysis analyzes the apps at runtime to provide actual evidence of their behavior. However, these techniques are harder to scale as they need to be run on an instrumented device to collect runtime data. Similarly, there is a need to stimulate the app, simulating real inputs to examine as many code-paths as possible. While there are some automatic techniques to generate synthetic inputs, they have been shown to be insufficient. In this thesis, we explore the benefits of combining static and dynamic analysis techniques to complement each other and reduce their limitations. While most previous work has often relied on using these techniques in isolation, we combine their strengths in different and novel ways that allow us to further study different privacy issues on the Android ecosystem. Namely, we demonstrate the potential of combining these complementary methods to study three inter-related issues: • A regulatory analysis of parental control apps. We use a novel methodology that relies on easy-to-scale static analysis techniques to pin-point potential privacy issues and violations of current legislation by Android apps and their embedded SDKs. We rely on the results from our static analysis to inform the way in which we manually exercise the apps, maximizing our ability to obtain real evidence of these misbehaviors. We study 46 publicly available apps and find instances of data collection and sharing without consent and insecure network transmissions containing personal data. We also see that these apps fail to properly disclose these practices in their privacy policy. • A security analysis of the unauthorized access to permission-protected data without user consent. We use a novel technique that combines the strengths of static and dynamic analysis, by first comparing the data sent by applications at runtime with the permissions granted to each app in order to find instances of potential unauthorized access to permission protected data. Once we have discovered the apps that are accessing personal data without permission, we statically analyze their code in order to discover covert- and side-channels used by apps and SDKs to circumvent the permission system. This methodology allows us to discover apps using the MAC address as a surrogate for location data, two SDKs using the external storage as a covert-channel to share unique identifiers and an app using picture metadata to gain unauthorized access to location data. • A novel SDK detection methodology that relies on obtaining signals observed both in the app’s code and static resources and during its runtime behavior. Then, we rely on a tree structure together with a confidence based system to accurately detect SDK presence without the need of any a priory knowledge and with the ability to discern whether a given SDK is part of legacy or dead code. We prove that this novel methodology can discover third-party SDKs with more accuracy than state-of-the-art tools both on a set of purpose-built ground-truth apps and on a dataset of 5k publicly available apps. With these three case studies, we are able to highlight the benefits of combining static and dynamic analysis techniques for the study of the privacy and security guarantees and risks of Android apps and third-party SDKs. The use of these techniques in isolation would not have allowed us to deeply investigate these privacy issues, as we would lack the ability to provide real evidence of potential breaches of legislation, to pin-point the specific way in which apps are leveraging cover and side channels to break Android’s permission system or we would be unable to adapt to an ever-changing ecosystem of Android third-party companies.The works presented in this thesis were partially funded within the framework of the following projects and grants: • European Union’s Horizon 2020 Innovation Action program (Grant Agreement No. 786741, SMOOTH Project and Grant Agreement No. 101021377, TRUST AWARE Project). • Spanish Government ODIO NºPID2019-111429RB-C21/PID2019-111429RBC22. • The Spanish Data Protection Agency (AEPD) • AppCensus Inc.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Srdjan Matic.- Secretario: Guillermo Suárez-Tangil.- Vocal: Ben Stoc

On the Security and Privacy Challenges in Android-based Environments

In the last decade, we have faced the rise of mobile devices as a fundamental tool in our everyday life. Currently, there are above 6 billion smartphones, and 72% of them are Android devices. The functionalities of smartphones are enriched by mobile apps through which users can perform operations that in the past have been made possible only on desktop/laptop computing. Besides, users heavily rely on them for storing even the most sensitive information from a privacy point of view. However, apps often do not satisfy all minimum security requirements and can be targeted to indirectly attack other devices managed or connected to them (e.g., IoT nodes) that may perform sensitive operations such as health checks, control a smart car or open a smart lock. This thesis discusses some research activities carried out to enhance the security and privacy of mobile apps by i) proposing novel techniques to detect and mitigate security vulnerabilities and privacy issues, and ii) defining techniques devoted to the security evaluation of apps interacting with complex environments (e.g., mobile-IoT-Cloud). In the first part of this thesis, I focused on the security and privacy of Mobile Apps. Due to the widespread adoption of mobile apps, it is relatively straightforward for researchers or users to quickly retrieve the app that matches their tastes, as Google provides a reliable search engine. However, it is likewise almost impossible to select apps according to a security footprint (e.g., all apps that enforce SSL pinning). To overcome this limitation, I present APPregator, a platform that allows users to select apps according to a specific security footprint. This tool aims to implement state-of-the-art static and dynamic analysis techniques for mobile apps and provide security researchers and analysts with a tool that makes it possible to search for mobile applications under specific functional or security requirements. Regarding the security status of apps, I studied a particular context of mobile apps: hybrid apps composed of web technologies and native technologies (i.e., Java or Kotlin). In this context, I studied a vulnerability that affected only hybrid apps: the Frame Confusion. This vulnerability, despite being discovered several years ago, it is still very widespread. I proposed a methodology implemented in FCDroid that exploits static and dynamic analysis techniques to detect and trigger the vulnerability automatically. The results of an extensive analysis carried out through FCDroid on a set of the most downloaded apps from the Google Play Store prove that 6.63% (i.e., 1637/24675) of hybrid apps are potentially vulnerable to Frame Confusion. A side effect of the analysis I carried out through APPregator was suggesting that very few apps may have a privacy policy, despite Google Play Store imposes some strict rules about it and contained in the Google Play Privacy Guidelines. To empirically verify if that was the case, I proposed a methodology based on the combination of static analysis, dynamic analysis, and machine learning techniques. The proposed methodology verifies whether each app contains a privacy policy compliant with the Google Play Privacy Guidelines, and if the app accesses privacy-sensitive information only upon the acceptance of the policy by the user. I then implemented the methodology in a tool, 3PDroid, and evaluated a number of recent and most downloaded Android apps in the Google Play Store. Experimental results suggest that over 95% of apps access sensitive user privacy information, but only a negligible subset of it (~ 1%) fully complies with the Google Play Privacy Guidelines. Furthermore, the obtained results have also suggested that the user privacy could be put at risk by mobile apps that keep collecting a plethora of information regarding the user's and the device behavior by relying on third-party analytics libraries. However, collecting and using such data raised several privacy concerns, mainly because the end-user - i.e., the actual data owner - is out of the loop in this collection process. The existing privacy-enhanced solutions that emerged in the last years follow an ``all or nothing" approach, leaving to the user the sole option to accept or completely deny access to privacy-related data. To overcome the current state-of-the-art limitations, I proposed a data anonymization methodology, called MobHide, that provides a compromise between the usefulness and privacy of the data collected and gives the user complete control over the sharing process. For evaluating the methodology, I implemented it in a prototype called HideDroid and tested it on 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. In the second part of this thesis, I extended privacy and security considerations outside the boundary of the single mobile device. In particular, I focused on two scenarios. The first is composed of an IoT device and a mobile app that have a fruitful integration to resolve and perform specific actions. From a security standpoint, this leads to a novel and unprecedented attack surface. To deal with such threats, applying state-of-the-art security analysis techniques on each paradigm can be insufficient. I claimed that novel analysis methodologies able to systematically analyze the ecosystem as a whole must be put forward. To this aim, I introduced the idea of APPIoTTe, a novel approach to the security testing of Mobile-IoT hybrid ecosystems, as well as some notes on its implementation working on Android (Mobile) and Android Things (IoT) applications. The second scenario is composed of an IoT device widespread in the Smart Home environment: the Smart Speaker. Smart speakers are used to retrieving information, interacting with other devices, and commanding various IoT nodes. To this aim, smart speakers typically take advantage of cloud architectures: vocal commands of the user are sampled, sent through the Internet to be processed, and transmitted back for local execution, e.g., to activate an IoT device. Unfortunately, even if privacy and security are enforced through state-of-the-art encryption mechanisms, the features of the encrypted traffic, such as the throughput, the size of protocol data units, or the IP addresses, can leak critical information about the users' habits. In this perspective, I showcase this kind of risk by exploiting machine learning techniques to develop black-box models to classify traffic and implement privacy leaking attacks automatically

GroupDroid: Automatically Grouping Mobile Malware by Extracting Code Similarities

As shown in previous work, malware authors often reuse portions of code in the development of their samples. Especially in the mobile scenario, there exists a phenomena, called piggybacking, that describes the act of embedding malicious code inside benign apps. In this paper, we leverage such observations to analyze mobile malware by looking at its similarities. In practice, we propose a novel approach that identifies and extracts code similarities in mobile apps. Our approach is based on static analysis and works by computing the Control Flow Graph of each method and encoding it in a feature vector used to measure similarities. We implemented our approach in a tool, GroupDroid, able to group mobile apps together according to their code similarities. Armed with GroupDroid, we then analyzed modern mobile malware samples. Our experiments show that GroupDroid is able to correctly and accurately distinguish different malware variants, and to provide useful and detailed information about the similar portions of malicious code

Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work