Wenrui Diao

Zidong Zhang, Zhentao Xie, Lingyun Ying et al.

Mini-programs have become a dominant paradigm for lightweight application deployment within super apps such as WeChat. To support seamless integration, super apps provide OAuth mechanisms for user login. However, improper integration of OAuth-based Authentication (OBA) flows by third-party developers can lead to critical security flaws. In this paper, we discover three new types of runtime OBA misuses that differ from prior static-code-based studies, enabling attackers to impersonate victims. To assess their real-world impact, we design and implement MINIAUTH, the first analysis framework for systematically analyzing OBA misuse at scale. MINIAUTH automatically pinpoints the OBA login page of a mini-program, executes the workflow dynamically, and analyzes its runtime behaviors. This enables it to handle obfuscated mini-programs and uncover vulnerabilities that existing approaches cannot detect. Applying MINIAUTH to 44,273 WeChat and 2,721 Baidu mini-programs, we uncover 1,834 misuse cases, including critical logic flaws that enable client-side identity forgery via exposed credentials and authentication bypass through static or plaintext identifiers. Our cross-platform evaluation further shows that such misuses are not confined to a single ecosystem but consistently appear across different mini-program platforms. We also identify a cryptographic design flaw in Baidu's OBA APIs that allows brute-forcing of session keys. We responsibly disclosed our findings to the developers and platforms, receiving acknowledgments and assigned CNVD/CNNVD IDs. These results underscore the need for more robust developer guidance and enhanced platform-level safeguards.

Rui Li, Wenrui Diao, Debin Gao

Android has adopted Kotlin alongside Java across apps and core system components. During this shift, we observe parallel implementations in the Android Open Source Project (AOSP) where the same component is implemented in both Java and Kotlin. In principle, their functional purposes are identical. In practice, subtle semantic divergences can appear. Such divergences are not vulnerabilities by themselves, but they provide useful clues that may reveal flaws in surrounding enforcement logic. To the best of our knowledge, this paper presents the first systematic study of Java-Kotlin parallel implementations in the Android framework and examines their security implications. We design and build ParaDroid, an analysis framework that identifies parallel methods at scale and compares their behaviors. ParaDroid normalizes code into a bytecode-level intermediate representation, reconstructs class-to-source mappings, and uses large language models to reason about method semantics and identify behavioral divergences. Evaluated on AOSP Android 14-16, ParaDroid identified 329 parallel method pairs and 37 vulnerable divergences. We responsibly disclosed the exploitable issues to the Android Security Team. Three vulnerabilities and two bugs have been confirmed, and two CVE IDs have been assigned. Our results demonstrate that parallel Java-Kotlin code paths provide a practical surface for discovering security flaws in modern Android.

Zidong Zhang, Zhentao Xie, Wenrui Diao et al.

Third-party mobile agents powered by Vision-Language Models (VLMs) have emerged as a promising paradigm for automating smartphone interactions. These agents act as high-privilege decision-makers, perceiving device states through screenshots and executing actions via VLM reasoning, transforming how an agent app interacts with the environment (i.e., other apps or the OS). Correspondingly, this transformation introduces new attack surfaces or transforms benign/harmless interfaces into exploitable ones for mobile devices. In this paper, we summarize key differences between third-party mobile agent apps and general apps when interacting with the environment, analyze the security posture of agents, and identify two unique attack surfaces compared to general mobile apps: the Screen Perception Attack Surface, which exploits the gap between human and machine vision, and the Misused Channel Attack Surface, which intercepts or manipulates the agent's execution pipeline. We design and implement seven concrete attacks, from subliminal text injection and invisible pixel zone exploitation to screenshot tampering and host PC command injection. Our evaluation of five popular mobile agent frameworks demonstrates that a malicious app can hijack agent actions and achieve arbitrary command execution even without any privilege permissions, while remaining visually indistinguishable to users. These findings reveal a fundamental trust mismatch in autonomous agent design and highlight the urgent need for perception-aware security models on multi-tenant platforms.

Siyu Shen, Fenghao Xu, Wenrui Diao et al.

Screenshot-based mobile GUI agents can operate ordinary smartphone apps through the same visual interface as a human user, but this capability also turns every screen observation into a privacy boundary. During normal task execution, screenshots may expose contacts, messages, photos, files, recommendations, health cues, and other sensitive context that is unrelated to the user's request. We call this problem incidental visual privacy exposure. It is difficult to address with existing defenses: text anonymization misses many visual and inferential cues, while generic privacy masking can remove the evidence and controls that a GUI agent needs to complete the task. This paper presents CAPED, a context-aware pre-upload exposure control layer for mobile GUI agents. CAPED is designed as a phone-side protection layer: before screenshots are released to a remote multimodal agent, it extracts task requirements, uses screen context as a privacy prior, parses visible UI elements, and selectively exposes only content needed for the current task while masking incidental private content. We evaluate CAPED on AndroidWorld for broad task utility and with a controlled 28-task seeded privacy evaluation used as a measurement instrument for trajectory-level incidental leakage. In this seeded evaluation, Full CAPED reduces success-conditioned weighted seeded leakage from 0.766 under raw screenshots to 0.268 while preserving high task utility. A broader AndroidWorld run shows a remaining prototype-level utility cost, but the results support the central claim that screenshot upload should be treated as an explicit device--cloud boundary decision, governed by task-driven selective exposure rather than all-or-nothing screen sharing.

Chao Chen, Wenrui Diao, Yingpei Zeng et al.

The Secure Sockets Layer (SSL) and Transport Layer Security (TLS) protocols are the foundation of network security. The certificate verification in SSL/TLS implementations is vital and may become the weak link in the whole network ecosystem. In previous works, some research focused on the automated testing of certificate verification, and the main approaches rely on generating massive certificates through randomly combining parts of seed certificates for fuzzing. Although the generated certificates could meet the semantic constraints, the cost is quite heavy, and the performance is limited due to the randomness. To fill this gap, in this paper, we propose DRLGENCERT, the first framework of applying deep reinforcement learning to the automated testing of certificate verification in SSL/TLS implementations. DRLGENCERT accepts ordinary certificates as input and outputs newly generated certificates which could trigger discrepancies with high efficiency. Benefited by the deep reinforcement learning, when generating certificates, our framework could choose the best next action according to the result of a previous modification, instead of simple random combinations. At the same time, we developed a set of new techniques to support the overall design, like new feature extraction method for X.509 certificates, fine-grained differential testing, and so forth. Also, we implemented a prototype of DRLGENCERT and carried out a series of real-world experiments. The results show DRLGENCERT is quite efficient, and we obtained 84,661 discrepancy-triggering certificates from 181,900 certificate seeds, say around 46.5% effectiveness. Also, we evaluated six popular SSL/TLS implementations, including GnuTLS, MatrixSSL, MbedTLS, NSS, OpenSSL, and wolfSSL. DRLGENCERT successfully discovered 23 serious certificate verification flaws, and most of them were previously unknown.

Shuaike Dong, Menghao Li, Wenrui Diao et al.

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches: identifier renaming, string encryption, Java reflection, and packing. To obtain the meaningful statistical results, we designed efficient and lightweight detection models for each obfuscation technique and applied them to our massive APK datasets (collected from Google Play, multiple third-party markets, and malware databases). We have learned several interesting facts from the result. For example, malware authors use string encryption more frequently, and more apps on third-party markets than Google Play are packed. We are also interested in the explanation of each finding. Therefore we carry out in-depth code analysis on some Android apps after sampling. We believe our study will help developers select the most suitable obfuscation approach, and in the meantime help researchers improve code analysis systems in the right direction.

Nan Zhang, Soteris Demetriou, Xianghang Mi et al.

Inspired by the boom of the consumer IoT market, many device manufacturers, start-up companies and technology giants have jumped into the space. Unfortunately, the exciting utility and rapid marketization of IoT, come at the expense of privacy and security. Industry reports and academic work have revealed many attacks on IoT systems, resulting in privacy leakage, property loss and large-scale availability problems. To mitigate such threats, a few solutions have been proposed. However, it is still less clear what are the impacts they can have on the IoT ecosystem. In this work, we aim to perform a comprehensive study on reported attacks and defenses in the realm of IoT aiming to find out what we know, where the current studies fall short and how to move forward. To this end, we first build a toolkit that searches through massive amount of online data using semantic analysis to identify over 3000 IoT-related articles. Further, by clustering such collected data using machine learning technologies, we are able to compare academic views with the findings from industry and other sources, in an attempt to understand the gaps between them, the trend of the IoT security risks and new problems that need further attention. We systemize this process, by proposing a taxonomy for the IoT ecosystem and organizing IoT security into five problem areas. We use this taxonomy as a beacon to assess each IoT work across a number of properties we define. Our assessment reveals that relevant security and privacy problems are far from solved. We discuss how each proposed solution can be applied to a problem area and highlight their strengths, assumptions and constraints. We stress the need for a security framework for IoT vendors and discuss the trend of shifting security liability to external or centralized entities. We also identify open research problems and provide suggestions towards a secure IoT ecosystem.

Zhe Zhou, Wenrui Diao, Xiangyu Liu et al.

In this paper, we present that security threats coming with existing GPU memory management strategy are overlooked, which opens a back door for adversaries to freely break the memory isolation: they enable adversaries without any privilege in a computer to recover the raw memory data left by previous processes directly. More importantly, such attacks can work on not only normal multi-user operating systems, but also cloud computing platforms. To demonstrate the seriousness of such attacks, we recovered original data directly from GPU memory residues left by exited commodity applications, including Google Chrome, Adobe Reader, GIMP, Matlab. The results show that, because of the vulnerable memory management strategy, commodity applications in our experiments are all affected.

Xiangyu Liu, Zhe Zhou, Wenrui Diao et al.

With millions of apps that can be downloaded from official or third-party market, Android has become one of the most popular mobile platforms today. These apps help people in all kinds of ways and thus have access to lots of user's data that in general fall into three categories: sensitive data, data to be shared with other apps, and non-sensitive data not to be shared with others. For the first and second type of data, Android has provided very good storage models: an app's private sensitive data are saved to its private folder that can only be access by the app itself, and the data to be shared are saved to public storage (either the external SD card or the emulated SD card area on internal FLASH memory). But for the last type, i.e., an app's non-sensitive and non-shared data, there is a big problem in Android's current storage model which essentially encourages an app to save its non-sensitive data to shared public storage that can be accessed by other apps. At first glance, it seems no problem to do so, as those data are non-sensitive after all, but it implicitly assumes that app developers could correctly identify all sensitive data and prevent all possible information leakage from private-but-non-sensitive data. In this paper, we will demonstrate that this is an invalid assumption with a thorough survey on information leaks of those apps that had followed Android's recommended storage model for non-sensitive data. Our studies showed that highly sensitive information from billions of users can be easily hacked by exploiting the mentioned problematic storage model. Although our empirical studies are based on a limited set of apps, the identified problems are never isolated or accidental bugs of those apps being investigated. On the contrary, the problem is rooted from the vulnerable storage model recommended by Android. To mitigate the threat, we also propose a defense framework.

Wenrui Diao, Xiangyu Liu, Zhe Zhou et al.

Previous research about sensor based attacks on Android platform focused mainly on accessing or controlling over sensitive device components, such as camera, microphone and GPS. These approaches get data from sensors directly and need corresponding sensor invoking permissions. This paper presents a novel approach (GVS-Attack) to launch permission bypassing attacks from a zero permission Android application (VoicEmployer) through the speaker. The idea of GVS-Attack utilizes an Android system built-in voice assistant module -- Google Voice Search. Through Android Intent mechanism, VoicEmployer triggers Google Voice Search to the foreground, and then plays prepared audio files (like "call number 1234 5678") in the background. Google Voice Search can recognize this voice command and execute corresponding operations. With ingenious designs, our GVS-Attack can forge SMS/Email, access privacy information, transmit sensitive data and achieve remote control without any permission. Also we found a vulnerability of status checking in Google Search app, which can be utilized by GVS-Attack to dial arbitrary numbers even when the phone is securely locked with password. A prototype of VoicEmployer has been implemented to demonstrate the feasibility of GVS-Attack in real world. In theory, nearly all Android devices equipped with Google Services Framework can be affected by GVS-Attack. This study may inspire application developers and researchers rethink that zero permission doesn't mean safety and the speaker can be treated as a new attack surface.

Zhe Zhou, Wenrui Diao, Xiangyu Liu et al.

The popularity of mobile device has made people's lives more convenient, but threatened people's privacy at the same time. As end users are becoming more and more concerned on the protection of their private information, it is even harder to track a specific user using conventional technologies. For example, cookies might be cleared by users regularly. Apple has stopped apps accessing UDIDs, and Android phones use some special permission to protect IMEI code. To address this challenge, some recent studies have worked on tracing smart phones using the hardware features resulted from the imperfect manufacturing process. These works have demonstrated that different devices can be differentiated to each other. However, it still has a long way to go in order to replace cookie and be deployed in real world scenarios, especially in terms of properties like uniqueness, robustness, etc. In this paper, we presented a novel method to generate stable and unique device ID stealthy for smartphones by exploiting the frequency response of the speaker. With carefully selected audio frequencies and special sound wave patterns, we can reduce the impacts of non-linear effects and noises, and keep our feature extraction process un-noticeable to users. The extracted feature is not only very stable for a given smart phone speaker, but also unique to that phone. The feature contains rich information that is equivalent to around 40 bits of entropy, which is enough to identify billions of different smart phones of the same model. We have built a prototype to evaluate our method, and the results show that the generated device ID can be used as a replacement of cookie.