Mobile super apps, which bundle multiple mini-apps into a single platform, have become central to the consumer-facing digital ecosystem. Services such as WeChat, Alipay, Grab, and TikTok integrate payments, messaging, commerce, and entertainment, while at the same time collecting and processing large volumes of sensitive personal data. This concentration of functionality creates unprecedented opportunities to businesses and online services but also raises significant security and privacy risks. Meanwhile, a growing trend is the integration of large language models (LLMs) into mobile apps, transforming them into LLM-driven agentic systems. These systems are capable of orchestrating mini-apps and other mobile apps, interacting with external services, and carrying out privileged tasks on behalf of users. While this enables powerful new applications, it also expands the attack surface and introduces new forms of data exposure, misuse of privileges, and adversarial manipulation. The workshop seeks contributions including but not limited secure architectural design, permission frameworks, threat modeling, privacy-preserving methods, and case studies of real-world deployments in order to build a foundation for safer and more trustworthy LLM-empowered super apps and more generally mobile apps.

Token-inconsistency bugs (TIBs) involve the misuse of syntactically valid yet incorrect code tokens, such as misused variables and erroneous function invocations, which can often lead to software bugs. Unlike simple syntactic bugs, TIBs occur at the semantic level and are subtle - sometimes they remain undetected for years. Traditional detection methods, such as static analysis and dynamic testing, often struggle with TIBs due to their versatile and context-dependent nature. However, advancements in large language models (LLMs) like GPT-4 present new opportunities for automating TIB detection by leveraging these models’ semantic understanding capabilities.This paper reports the first systematic measurement of LLMs’ capabilities in detecting TIBs, revealing that while GPT-4 shows promise, it exhibits limitations in precision and scalability. Specifically, its detection capability is undermined by the model’s tendency to focus on the code snippets that do not contain TIBs; its scalability concern arises from GPT-4’s high cost and the massive amount of code requiring inspection. To address these challenges, we introduce LineBreaker, a novel and cascaded TIB detection system. LineBreaker leverages smaller, codespecific, and highly efficient language models to filter out large numbers of code snippets unlikely to contain TIBs, thereby significantly enhancing the system’s performance in terms of precision, recall, and scalability. We evaluated LineBreaker on 154 Python and C GitHub repositories, each with over 1,000 stars, uncovering 123 new flaws, 45% of which could be exploited to disrupt program functionalities. Out of our 69 submitted fixes, 41 have already been confirmed or merged.

Mobile super apps are revolutionizing mobile computing by offering diverse services through integrated "miniapps'', creating comprehensive ecosystems akin to app stores like Google Play and Apple's App Store. While these platforms, such as WeChat, Alipay, and TikTok, enhance user convenience and functionality, they also raise significant security and privacy concerns due to the vast amounts of user data they handle. In response, the Workshop on Secure and Trustworthy Superapps (SaTS 2024) aims to address these critical issues by fostering collaboration among researchers and practitioners to explore solutions that protect users and enhance security within the super app landscape.

Privacy label disclosure guideline, which specifies the data usage practices of third-party libraries (TPL), is a valuable resource for iOS app developers to accurately complete their iOS privacy labels. This is particularly important given the mandatory requirement for all apps on the App Store to disclose their data practices via privacy labels. However, it is essential to ensure the accuracy and compliance of these guidelines to ensure that accurate TPL data usage has been provided to app developers. Despite the significance of these guidelines, there is little understanding of how accurate and compliant they are in reflecting the actual data practices of third-party libraries used in iOS apps. To address this issue, our study implements a tool called Colaine to automatically check the compliance of privacy label disclosure guidelines, taking into account the configurable data practices in TPLs. Colaine analyzed 107 TPLs associated with 1,605 different configurations, shedding light on the prevalence and seriousness of privacy label disclosure guideline non-compliance issues.

IoT messaging protocols are critical to connecting users and IoT devices. Among all the protocols, the Message Queuing and Telemetry Transport (MQTT) is arguably the most widely used. Mainstream IoT platforms leverage MQTT brokers, server side implementation of MQTT, to enable and mediate user-device communication (e.g., the transmission of control commands). There are over 70 open-source MQTT brokers, which have been widely adopted in production. Any security defects in those open-source MQTT brokers easily get into many vendors’ IoT deployments with amplified impacts, inevitably endangering the security of IoT applications and millions of users. We report the first systematic security analysis of open-source MQTT brokers in the wild. To enable the analysis, we designed and developed MQTTactic, a semi-automatic tool that can formally verify MQTT broker implementations based on generated security properties. MQTTactic is based on static code analysis, formal modeling, and automated model checking (with off-the-shelf model checker Spin). In designing MQTTactic, we characterize and address key technical challenges. MQTTactic currently focuses on authorization-related properties, and discovered 7 novel, zero-day flaws practically enabling serious, unauthorized access. We reported all flaws to related parties, who acknowledged the issues and have been taking actions to fix them. Our thorough evaluation shows that MQTTactic is effective and practical.

Research on side-channel leaks has long been focusing on the information exposure from a single channel (memory, network traffic, power, etc.). Less studied is the risk of learning from multiple side channels related to a target activity (e.g., website visits) even when individual channels are not informative enough for an effective attack. Although the prior research made the first step on this direction, inferring the operations of foreground apps on iOS from a set of global statistics, still less clear are how to determine the maximum information leaks from all target-related side channels on a system, what can be learnt about the target from such leaks and most importantly, how to control information leaks from the whole system, not just from an individual channel. To answer these fundamental questions, we performed the first systematic study on multi-channel inference, focusing on iOS as the first step. Our research is based upon a novel attack technique, called Mischief, which given a set of potential side channels related to a target activity (e.g., foreground apps), utilizes probabilistic search to approximate an optimal subset of the channels exposing most information, as measured by Merit Score, a metric for correlation-based feature selection. On such an optimal subset, an inference attack is modeled as a multivariate time series classification problem, so the state-of-the-art deep-learning based solution, InceptionTime in particular, can be applied to achieve the best possible outcome. Mischief is found to work effectively on today's iOS (16.2), identifying foreground apps, website visits, sensitive IoT operations (e.g., opening the door) with a high confidence, even in an open-world scenario, which demonstrates that the protection Apple puts in place against the known attack is inadequate. Also importantly, this new understanding enables us to develop more comprehensive protection, which could elevate today's side-channel research from suppressing leaks from individual channels to controlling information exposure across the whole system.

Emerging IoT clouds are playing a more important role in modern lives, enabling users/developers to program applications to make better use of smart devices. However, preliminary research has shown IoT cloud vulnerabilities could expose IoT users to security risks. To better understand the problem, we studied the SmartThings cloud, one of the most popular IoT cloud platforms that support user-defined device automation (SmartApps). Specifically, we found new vulnerabilities in SmartThings that allow attackers to fake events to trigger the SmartApps to operate devices (e.g., open a lock). Exploiting such vulnerabilities, we successfully faked 7 different types of events, which impact 138 (out of 187) SmartThings’ official open-sourced SmartApps. To defeat such attacks, we propose an authenticity-verification-based scheme to deny fake events. Moreover, we designed a tool,SmartPatch, to help users secure their SmartThings systems. In specific,SmartPatchautomatically patches the vulnerable SmartApps and Device Handlers (input) and outputs the flawless programs, which are ready for users to deploy in their SmartThings systems. We have madeSmartPatchpublicly available. With the help ofSmartPatch, we patched all the vulnerable SmartThings’ official open-sourced programs (146 SmartApps and 321 Device Handlers). Experiments have shown the compatibility, effectiveness, and efficiency of our proposed approach.

Modern IoT device manufacturers are taking advantage of the managed Platform-as-a-Service (PaaS) and Infrastructure-as-a-Service (IaaS) IoT clouds (e.g., AWS IoT, Azure IoT) for secure and convenient IoT development/deployment. The IoT access control is achieved by manufacturer-specified, cloud-enforced IoT access policies (cloud-standard JSON documents, called IoT Policies) stating which users can access which IoT devices/resources under what constraints. In this paper, we performed a systematic study on the security of cloud-based IoT access policies on modern PaaS/IaaS IoT clouds. Our research shows that the complexity in the IoT semantics and enforcement logic of the policies leaves tremendous space for device manufacturers to program a flawed IoT access policy, introducing convoluted logic flaws which are non-trivial to reason about. In addition to challenges/mistakes in the design space, it is astonishing to find that mainstream device manufacturers also generally make critical mistakes in deploying IoT Policies thanks to the flexibility offered by PaaS/IaaS clouds and the lack of standard practices for doing so. Our assessment of 36 device manufacturers and 310 open-source IoT projects highlights the pervasiveness and seriousness of the problems, which once exploited, can have serious impacts on IoT users' security, safety, and privacy. To help manufacturers identify and easily fix IoT Policy flaws, we introduce P-Verifier, a formal verification tool that can automatically verify cloud-based IoT Policies. With evaluated high effectiveness and low performance overhead, P-Verifier will contribute to elevating security assurance in modern IoT deployments and access control. We responsibly reported all findings to affected vendors and fixes were deployed or on the way.

Mobile-as-a-Gateway (MaaG) is a popular feature using mobile devices as gateways to connect IoT devices to cloud services for management. MaaG IoT access control systems support remote access sharing/revocation while allowing "offline availability'' for better usability. Realizing these functionalities requires secure cooperation among the cloud service, the companion app, and the IoT device. For practical considerations, we find that almost all cloud services perform access model translation (AMT) to translate expressive cloud-side access policies to simple device-side policies. During the process, ad-hoc protocols are developed to support the access policy synchronization. Unfortunately, current MaaG IoT systems fail to recognize the security risks in the process of access model translation and synchronization. We analyze ten top-of-the-line MaaG IoT devices and find that all of them have serious vulnerabilities, e.g., allowing irrevocable and permanent access for temporary users. We further propose a secure protocol design that defends against all identified attacks.

The recent wave of in-browser cryptojacking has ebbed away, due to the new updates of mainstream cryptocurrrencies, which demand the level of mining resources browsers cannot afford. As replacements, resource-rich, loosely protected free Internet services, such as Continuous Integration (CI) platforms, have become attractive targets. In this paper, we report a systematic study on real-world illicit cryptomining on public CI platforms (called Cijacking). Unlike in-browser cryptojacking, Cijacks masquerade as CI jobs and are therefore more difficult to detect, since legitimate CI workflows such as container image building and testing also entail intensive computing. In our research, we leveraged the critical mining information the adversary has to specify, such as wallet addresses and mining pool domains, to recover the attack traces from GitHub repositories and the log files on CI platforms, leading to the discovery of 1,974 Cijacking instances, 30 campaigns across 12 different cryptocurrencies on 11 mainstream CI platforms. Further, our study unveils the evolution of attack strategies, in response to the protection put in place by the platforms, the duration of the mining jobs (as long as 33 months), and their lifecycle. Further discovered is the revenue of the attack, over ${\$}$20,000 per month. Since robust detection of cryptojacking is known to be hard, we developed a novel technique, called Cijitter, to strategically inject delays to the execution of a CI workflow to disproportionally penalize the mining jobs that need to work on a series of tasks under time constraints. Our analysis and evaluation, as conducted on both benchmarks and common CI jobs, show that our approach substantially suppresses the miner’s revenues, rendering them unprofitable, but only has small impacts on the performance of CI jobs and developer productivity (94.3% of CI jobs see a less than 10% delay).

An IoT device today can be managed through different channels, e.g., by its device manufacturer's app, or third-party channels such as Apple's Home app, or a smart speaker. Supporting each channel is a management framework integrated in the device and provided by different parties. For example, a device that integrates Apple HomeKit framework can be managed by Apple Home app. We call the management framework of this kind, including all its device- and cloud-side components, a device management channel (DMC). 4 third-party DMCs are widely integrated in today's IoT devices along with the device manufacturer's own DMC: HomeKit, Zigbee/Z-Wave compatible DMC, and smart-speaker Seamless DMC. Each of these DMCs is a standalone system that has full mandate on the device; however, if their security policies and control are not aligned, consequences can be serious, allowing a malicious user to utilize one DMC to bypass the security control imposed by the device owner on another DMC. We call such a problem Chaotic Device Management (Codema).

Drivers on Apple OSes (e.g., iOS, tvOS, iPadOS, macOS, etc.) run in the kernel space and driver vulnerabilities can incur serious security consequences. A recent report from Google Project Zero shows that driver vulnerabilities on Apple OSes have been actively exploited in the wild. Also, we observed that driver vulnerabilities have accounted for one-third of kernel bugs in recent iOS versions based on Apple's security updates. Despite the serious security implications, systematic static analysis on Apple drivers for finding security vulnerabilities has never been done before, not to mention any large-scale study of Apple drivers.

App-in-app is a new and trending mobile computing paradigm in which native app-like software modules, called sub-apps, are hosted by popular mobile apps such as Wechat, Baidu, TikTok and Chrome, to enrich the host app's functionalities and to form an "all-in-one app" ecosystem. Sub-apps access system resources through the host, and their functionalities come close to regular mobile apps (taking photos, recording voices, banking, shopping, etc.). Less clear, however, is whether the host app, typically a third-party app, is capable of securely managing sub-apps and their access to system resources. In this paper, we report the first systematic study on the resource management in app-in-app systems. Our study reveals high-impact security flaws, which allow the adversary to stealthily escalate privilege (e.g., accessing the camera, photo gallery, microphone, etc.) or acquire sensitive data (e.g., location, passwords of Amazon, Google, etc.). To understand the impacts of those flaws, we developed an analysis tool that automatically assesses 11 popular app-in-app platforms on both Android and iOS. Our results brought to light the prevalence of the security flaws. We further discuss the lessons learned and propose mitigation strategies.

To use library APIs, a developer is supposed to follow guidance and respect some constraints, which we call integration assumptions (IAs). Violations of these assumptions can have serious consequences, introducing security-critical flaws such as use-after-free, NULL-dereference, and authentication errors. Analyzing a program for compliance with IAs involves significant effort and needs to be automated. A promising direction is to automatically recover IAs from a library document using Natural Language Processing (NLP) and then verify their consistency with the ways APIs are used in a program through code analysis. However, a practical solution along this line needs to overcome several key challenges, particularly the discovery of IAs from loosely formatted documents and interpretation of their informal descriptions to identify complicated constraints (e.g., data-/control-flow relations between different APIs).

With the increasing popularity of the Internet of Things (IoT), many IoT cloud platforms have emerged to help the IoT manufacturers connect their devices to their users. Serving the device-user communication is general messaging protocol deployed on the platforms. Less clear, however, is whether such protocols, which are not designed to work in the adversarial environment of IoT, introduce new risks. In this paper, we report the first systematic study on the protection of major IoT clouds (e.g., AWS, Microsoft, IBM) put in place for the arguably most popular messaging protocol - MQTT. We found that these platforms' security additions to the protocol are all vulnerable, allowing the adversary to gain control of the device, launch a large-scale denial-of-service attack, steal the victim's secrets data and fake the victim's device status for deception. We successfully performed end-to-end attacks on these popular IoT clouds and further conducted a measurement study, which demonstrates that the security impacts of our attacks are real, severe and broad. We reported our findings to related parties, which all acknowledged the importance. We further propose new design principles and an enhanced access model MOUCON. We implemented our protection on a popular open-source MQTT server. Our evaluation shows its high effectiveness and negligible performance overhead.

The popularity of cloud hosting services also brings in new security chal- lenges: it has been reported that these services are increasingly utilized by miscreants for their malicious online activities. Mitigating this emerging threat, posed by such “bad repositories” (simply Bar), is challenging due to the different hosting strategy to traditional hosting service, the lack of direct observations of the repositories by those outside the cloud, the reluctance of the cloud provider to scan its customers’ repositories without their consent, and the unique evasion strategies employed by the adversary. In this paper, we took the first step toward understanding and detecting this emerging threat. Using a small set of “seeds” (i.e., confirmed Bars), we identified a set of collective features from the websites they serve (e.g., attempts to hide Bars), which uniquely characterize the Bars. These features were utilized to build a scanner that detected over 600 Bars on leading cloud platforms like Amazon, Google, and 150 K sites, including popular ones like , using them. Highlights of our study include the pivotal roles played by these repositories on malicious infrastructures and other important discoveries include how the adversary exploited legitimate cloud repositories and why the adversary uses Bars in the first place that has never been reported. These findings bring such malicious services to the spotlight and contribute to a better understanding and ultimately eliminating this new threat.

As a critical feature for enhancing user experience, cross-app URL invocation has been reported to cause unauthorized execution of app components. Although protection has already been put in place, little has been done to understand the security risks of navigating an app's WebView through an URL, a legitimate need for displaying the app's UI during cross-app interactions. In our research, we found that the current design of such cross-WebView navigation actually opens the door to a cross-app remote infection, allowing a remote adversary to spread malicious web content across different apps' WebView instances and acquire stealthy and persistent control of these apps. This new threat, dubbed Cross-App WebView Infection (XAWI), enables a series of multi-app, colluding attacks never thought before, with significant real world impacts. Particularly, we found that the remote adversary can collectively utilize multiple infected apps' individual capabilities to escalate his privileges on a mobile device or orchestrate a highly realistic remote Phishing attack (e.g., running a malicious script in Chrome to stealthily change Twitter's WebView to fake Twitter's own login UI). We show that the adversary can easily find such attack "building blocks" (popular apps whose WebViews can be redirected by another app) through an automatic fuzz, and discovered about 7.4% of the most popular apps subject to the XAWI attacks, including Facebook, Twitter, Amazon and others. Our study reveals the contention between the demand for convenient cross-WebView communication and the need for security control on the channel, and makes the first step toward building OS-level protection to safeguard this fast-growing technology.

The popularity of cloud hosting services also brings in new security challenges: it has been reported that these services are increasingly utilized by miscreants for their malicious online activities. Mitigating this emerging threat, posed by such "bad repositories" (simply Bar), is challenging due to the different hosting strategy to traditional hosting service, the lack of direct observations of the repositories by those outside the cloud, the reluctance of the cloud provider to scan its customers' repositories without their consent, and the unique evasion strategies employed by the adversary. In this paper, we took the first step toward understanding and detecting this emerging threat. Using a small set of "seeds" (i.e., confirmed Bars), we identified a set of collective features from the websites they serve (e.g., attempts to hide Bars), which uniquely characterize the Bars. These features were utilized to build a scanner that detected over 600 Bars on leading cloud platforms like Amazon, Google, and 150K sites, including popular ones like groupon.com, using them. Highlights of our study include the pivotal roles played by these repositories on malicious infrastructures and other important discoveries include how the adversary exploited legitimate cloud repositories and why the adversary uses Bars in the first place that has never been reported. These findings bring such malicious services to the spotlight and contribute to a better understanding and ultimately eliminating this new threat.

To adapt to the rapidly evolving landscape of cyber threats, security professionals are actively exchanging Indicators of Compromise (IOC) (e.g., malware signatures, botnet IPs) through public sources (e.g. blogs, forums, tweets, etc.). Such information, often presented in articles, posts, white papers etc., can be converted into a machine-readable OpenIOC format for automatic analysis and quick deployment to various security mechanisms like an intrusion detection system. With hundreds of thousands of sources in the wild, the IOC data are produced at a high volume and velocity today, which becomes increasingly hard to manage by humans. Efforts to automatically gather such information from unstructured text, however, is impeded by the limitations of today's Natural Language Processing (NLP) techniques, which cannot meet the high standard (in terms of accuracy and coverage) expected from the IOCs that could serve as direct input to a defense system. In this paper, we present iACE, an innovation solution for fully automated IOC extraction. Our approach is based upon the observation that the IOCs in technical articles are often described in a predictable way: being connected to a set of context terms (e.g., "download") through stable grammatical relations. Leveraging this observation, iACE is designed to automatically locate a putative IOC token (e.g., a zip file) and its context (e.g., "malware", "download") within the sentences in a technical article, and further analyze their relations through a novel application of graph mining techniques. Once the grammatical connection between the tokens is found to be in line with the way that the IOC is commonly presented, these tokens are extracted to generate an OpenIOC item that describes not only the indicator (e.g., a malicious zip file) but also its context (e.g., download from an external source). Running on 71,000 articles collected from 45 leading technical blogs, this new approach demonstrates a remarkable performance: it generated 900K OpenIOC items with a precision of 95% and a coverage over 90%, which is way beyond what the state-of-the-art NLP technique and industry IOC tool can achieve, at a speed of thousands of articles per hour. Further, by correlating the IOCs mined from the articles published over a 13-year span, our study sheds new light on the links across hundreds of seemingly unrelated attack instances, particularly their shared infrastructure resources, as well as the impacts of such open-source threat intelligence on security protection and evolution of attack strategies.

With the popularity of today's usability-oriented designs, dubbed Zero Configuration or ZeroConf, unclear are the security implications of these automatic service discovery, "plug-and-play" techniques. In this paper, we report the first systematic study on this issue, focusing on the security features of the systems related to Apple, the major proponent of ZeroConf techniques. Our research brings to light a disturbing lack of security consideration in these systems' designs: major ZeroConf frameworks on the Apple platforms, including the Core Bluetooth Framework, Multipeer Connectivity and Bonjour, are mostly unprotected and popular apps and system services, such as Tencent QQ, Apple Handoff, printer discovery and AirDrop, turn out to be completely vulnerable to an impersonation or Man-in-the-Middle (MitM) attack, even though attempts have been made to protect them against such threats. The consequences are serious, allowing a malicious device to steal the user's SMS messages, email notifications, documents to be printed out or transferred to another device. Most importantly, our study highlights the fundamental security challenges underlying ZeroConf techniques: in the absence of any pre-configured secret across different devices, authentication has to rely on Apple-issued public-key certificate, which however cannot be properly verified due to the difficulty in finding a unique, nonsensitive and widely known identity of a human user to bind her to her certificate. To address this issue, we developed a suite of new techniques, including a conflict detection approach and a biometric technique that enables the user to speak out her certificate through 6 distinct, rare but pronounceable words to let those who know her voice verify her certificate. We performed a security analysis on the new protection and evaluated its usability and effectiveness using two user studies involving 60 participants. Our research shows that the new protection fits well with the existing ZeroConf systems such as AirDrop. It is well received by users and also providing effective defense even against recently proposed speech synthesis attacks.

Promotional infection is an attack in which the adversary exploits a website's weakness to inject illicit advertising content. Detection of such an infection is challenging due to its similarity to legitimate advertising activities. An interesting observation we make in our research is that such an attack almost always incurs a great semantic gap between the infected domain (e.g., a university site) and the content it promotes (e.g., selling cheap viagra). Exploiting this gap, we developed a semantic-based technique, called Semantic Inconsistency Search (SEISE), for efficient and accurate detection of the promotional injections on sponsored top-level domains (sTLD) with explicit semantic meanings. Our approach utilizes Natural Language Processing (NLP) to identify the bad terms (those related to illicit activities like fake drug selling, etc.) most irrelevant to an sTLD's semantics. These terms, which we call irrelevant bad terms (IBTs), are used to query search engines under the sTLD for suspicious domains. Through a semantic analysis on the results page returned by the search engines, SEISE is able to detect those truly infected sites and automatically collect new IBTs from the titles/URLs/snippets of their search result items for finding new infections. Running on 403 sTLDs with an initial 30 seed IBTs, SEISE analyzed 100K fully qualified domain names (FQDN), and along the way automatically gathered nearly 600 IBTs. In the end, our approach detected 11K infected FQDN with a false detection rate of 1.5% and over 90% coverage. Our study shows that by effective detection of infected sTLDs, the bar to promotion infections can be substantially raised, since other non-sTLD vulnerable domains typically have much lower Alexa ranks and are therefore much less attractive for underground advertising. Our findings further bring to light the stunning impacts of such promotional attacks, which compromise FQDNs under 3% of .edu, .gov domains and over one thousand gov.cn domains, including those of leading universities such as stanford.edu, mit.edu, princeton.edu, havard.edu and government institutes such as nsf.gov and nih.gov. We further demonstrate the potential to extend our current technique to protect generic domains such as .com and .org.

On modern operating systems, applications under the same user are separated from each other, for the purpose of protecting them against malware and compromised programs. Given the complexity of today's OSes, less clear is whether such isolation is effective against different kind of cross-app resource access attacks (called XARA in our research). To better understand the problem, on the less-studied Apple platforms, we conducted a systematic security analysis on MAC OS~X and iOS. Our research leads to the discovery of a series of high-impact security weaknesses, which enable a sandboxed malicious app, approved by the Apple Stores, to gain unauthorized access to other apps' sensitive data. More specifically, we found that the inter-app interaction services, including the keychain, WebSocket and NSConnection on OS~X and URL Scheme on the MAC OS and iOS, can all be exploited by the malware to steal such confidential information as the passwords for iCloud, email and bank, and the secret token of Evernote. Further, the design of the app sandbox on OS~X was found to be vulnerable, exposing an app's private directory to the sandboxed malware that hijacks its Apple Bundle ID. As a result, sensitive user data, like the notes and user contacts under Evernote and photos under WeChat, have all been disclosed. Fundamentally, these problems are caused by the lack of app-to-app and app-to-OS authentications. To better understand their impacts, we developed a scanner that automatically analyzes the binaries of MAC OS and iOS apps to determine whether proper protection is missing in their code. Running it on hundreds of binaries, we confirmed the pervasiveness of the weaknesses among high-impact Apple apps. Since the issues may not be easily fixed, we built a simple program that detects exploit attempts on OS~X, helping protect vulnerable apps before the problems can be fully addressed.

Push messaging is among the most important mobile-cloud services, offering critical supports to a wide spectrum of mobile apps. This service needs to coordinate complicated interactions between developer servers and their apps in a large scale, making it error prone. With its importance, little has been done, however, to understand the security risks of the service. In this paper, we report the first security analysis on those push-messaging services, which reveals the pervasiveness of subtle yet significant security flaws in them, affecting billions of mobile users. Through even the most reputable services like Google Cloud Messaging (GCM) and Amazon Device Messaging (ADM), the adversary running carefully-crafted exploits can steal sensitive messages from a target device, stealthily install or uninstall any apps on it, remotely lock out its legitimate user or even completely wipe out her data. This is made possible by the vulnerabilities in those services' protection of device-to-cloud interactions and the communication between their clients and subscriber apps on the same devices. Our study further brings to light questionable practices in those services, including weak cloud-side access control and extensive use of PendingIntent, as well as the impacts of the problems, which cause popular apps or system services like Android Device Manager, Facebook, Google+, Skype, PayPal etc. to leak out sensitive user data or unwittingly act on the adversary's command. To mitigate this threat, we developed a technique that helps the app developers establish end-to-end protection of the communication with their apps, over the vulnerable messaging services they use.

Android is a fast evolving system, with new updates coming out one after another. These updates often completely overhaul a running system, replacing and adding tens of thousands of files across Android's complex architecture, in the presence of critical user data and applications (apps for short). To avoid accidental damages to such data and existing apps, the upgrade process involves complicated program logic, whose security implications, however, are less known. In this paper, we report the first systematic study on the Android updating mechanism, focusing on its Package Management Service (PMS). Our research brought to light a new type of security-critical vulnerabilities, called Pileup flaws, through which a malicious app can strategically declare a set of privileges and attributes on a low-version operating system (OS) and wait until it is upgraded to escalate its privileges on the new system. Specifically, we found that by exploiting the Pileup vulnerabilities, the app can not only acquire a set of newly added system and signature permissions but also determine their settings (e.g., protection levels), and it can further substitute for new system apps, contaminate their data (e.g., cache, cookies of Android default browser) to steal sensitive user information or change security configurations, and prevent installation of critical system services. We systematically analyzed the source code of PMS using a program verification tool and confirmed the presence of those security flaws on all Android official versions and over 3000 customized versions. Our research also identified hundreds of exploit opportunities the adversary can leverage over thousands of devices across different device manufacturers, carriers and countries. To mitigate this threat without endangering user data and apps during an upgrade, we also developed a new detection service, called SecUP, which deploys a scanner on the user's device to capture the malicious apps designed to exploit Pileup vulnerabilities, based upon the vulnerability-related information automatically collected from newly released Android OS images.

With the progress in mobile computing, web services are increasingly delivered to their users through mobile apps, instead of web browsers. However, unlike the browser, which enforces origin-based security policies to mediate the interactions between the web content from different sources, today's mobile OSes do not have a comparable security mechanism to control the cross-origin communications between apps, as well as those between an app and the web. As a result, a mobile user's sensitive web resources could be exposed to the harms from a malicious origin. In this paper, we report the first systematic study on this mobile cross-origin risk. Our study inspects the main cross-origin channels on Android and iOS, including intent, scheme and web-accessing utility classes, and further analyzes the ways popular web services (e.g., Facebook, Dropbox, etc.) and their apps utilize those channels to serve other apps. The research shows that lack of origin-based protection opens the door to a wide spectrum of cross-origin attacks. These attacks are unique to mobile platforms, and their consequences are serious: for example, using carefully designed techniques for mobile cross-site scripting and request forgery, an unauthorized party can obtain a mobile user's Facebook/Dropbox authentication credentials and record her text input. We report our findings to related software vendors, who all acknowledged their importance. To address this threat, we designed an origin-based protection mechanism, called Morbs, for mobile OSes. Morbs labels every message with its origin information, lets developers easily specify security policies, and enforce the policies on the mobile channels based on origins. Our evaluation demonstrates the effectiveness of our new technique in defeating unauthorized origin crossing, its efficiency and the convenience for the developers to use such protection.