Large language models(LLMs) are increasingly integrated with external systems through the Model Context Protocol(MCP),which standardizes tool invocation and has rapidly become a backbone for LLM-powered applications. While this paradigm enhances functionality,it also introduces a fundamental security shift:LLMs transition from passive information processors to autonomous orchestrators of task-oriented toolchains,expanding the attack surface,elevating adversarial goals from manipulating single outputs to hijacking entire execution flows. In this paper,we identify and characterize a systematic privacy-leakage attack pattern,termed Parasitic Toolchain Attacks,instantiated as MCP Unintended Privacy Disclosure(MCP-UPD). These attacks require no direct victim interaction;instead,adversaries embed malicious instructions into external data sources that LLMs access during legitimate tasks. Unlike traditional prompt injection and tool poisoning attacks,our attack targets the interconnected toolchain itself,assembling multiple legitimate tools into a coordinated workflow whose combined behavior accomplishes malicious objectives. In MCP-UPD,the malicious logic infiltrates the toolchain and unfolds in three phases:Parasitic Ingestion,Privacy Collection,and Privacy Disclosure,culminating in stealthy exfiltration of private data. Our root cause analysis reveals that MCP lacks both context-tool isolation and least-privilege enforcement,enabling adversarial instructions to propagate unchecked into sensitive tool invocations. To assess the severity,we design MCP-SEC and conduct the first large-scale security census of the MCP ecosystem,analyzing 12230 tools across 1360 servers. Our findings show that the MCP ecosystem is rife with real-world exploitable gadgets and diverse attack methods,underscoring systemic risks in MCP platforms and the urgent need for defense mechanisms in LLM-integrated environments.

For compatibility checks and preventing malicious cheating behaviors in Android systems, It is convenient for benign app developers to utilize emulation-detection technology. However, this technique has been abused by malicious app developers, which causes detection emulation and behavior change, known as anti-emulation behavior, to evade the dynamic analysis performed via the Android emulator. The Android permission mechanism can limit some anti-emulation behaviors, but attackers can still use permission-independent (PI) emulation-detection technology to achieve their goals. In this paper, we propose a static and dynamic combined detection framework namedPIEDCheckerto detect PI emulation-detection apps. This framework can statically identify PI emulation-detection code and dynamically verify PI anti-emulation behaviors.PIEDChecker's performance is validated by 382 manually created test apps and 344 apps with emulation-detection labels. Moreover,PIEDCheckerhas higher accuracy in detecting PI emulation-detection compared with the existing Android malware analysis platforms and academic methods. Moreover, it is found that there are 13,377 apps having PI emulation-detection behaviors within the tested 25,303 apps collected over the past five years. In particular, the detection features of the PI emulation-detection methods are summarized based on the evaluation result.

Anti-emulation is crucial for Android and IoT security as it helps apps determine whether they are running on a real mobile device or in an emulation environment. This prevents apps from being analyzed, debugged, or reverse-engineered in emulators, ultimately stopping criminals from making illegal profits. Current emulator detection methods cannot balance accuracy, universality, robustness, and compatibility. Their universality is often hindered by limited data diversity and accessibility. To address these issues, we propose the comprehensive Android emulator detection (CAED) framework. The Preprocessing Module of CAED collects and normalizes data from both phones and emulators. We propose the first data augmentation method for emulator detection, emulator detection augmentation generative adversarial network (EDA-GAN), which is tailored to the characteristics of our data and effectively enhances data diversity. The classifier module MFBoost employs an adaptive imputation algorithm and multiple classification and regression trees (CART) for precise classification. Experiments on 324 devices show that CAED improves detection rate by at least 12.5% and up to 44.71% over state-of-the-art (SOTA) methods. The EDA-GAN data augmentation method boosts classifier accuracy, achieving a performance of up to 99.62%. Additionally, CAED’s unique loss function and imputation algorithm enhance the robustness and compatibility of CAED, with a 24% smaller accuracy drop than other methods when features are modified or unavailable. This study presents the CAED framework as an effective solution for protecting apps against real-world security threats in Android and IoT environments.

Mini-programs are lightweight apps running in super apps (such as WeChat, Baidu, Alipay, and TikTok), an emerging paradigm in the era of mobile computing. With the growing popularity of mini-programs, there is an increasing concern for their security and privacy. In essence, mini-programs are WebView-based apps. This means that they may be vulnerable to the same security risks associated with web apps. In this work, we discovered a new mini-program vulnerability called MiniCPRF (Cross-Page Request Forgery in Mini-Programs). The exploit of this vulnerability is easy, and the attack consequences are severe, leading to unauthorized operations, such as free shopping, and the exposure of confidential information, such as credit card numbers. The root causes of MiniCPRF can be attributed to multiple design flaws in both mini-programs and their super apps, including the insecure routing mechanism, lack of message integrity check, and plain-text storage. To evaluate the impacts of MiniCPRF, we designed an automated analysis framework called MiniCAT. It can automatically crawl mini-programs, perform static analysis on them, and generate detection reports. In large-scale real-world evaluations with MiniCAT, we identified that 32.0% (13,349/41,726) of analyzable mini-programs are potentially vulnerable to MiniCPRF, including some famous ones with millions of users, such as Sohu and Wenjuanxing. Following the responsible disclosure principle, we have reported verified vulnerable mini-programs to the corresponding vendors and developers, and three real-world cases have been confirmed by CNVD. Additionally, we suggest mitigation strategies to resolve the security issue related to MiniCPRF.

Android applications (apps) widely use third-party libraries (TPLs) to reuse functionalities and simplify the development process. Unfortunately, these TPLs often suffer from vulnerabilities that attackers can exploit, leading to catastrophic consequences for app users. To mitigate this threat, researchers have developed tools to detect TPL versions in the app. If an app is found using a TPL vulnerable version, these tools will issue warnings. Although these tools claim to resist the effects of code obfuscation, our preliminary study indicates that code optimization is common during the app release process. A lack of consideration for the impact of code optimizations significantly reduces the effectiveness of existing tools. To fill this gap, this work systematically investigates how and to what extent different optimization strategies affect existing tools. Our findings have led to a new tool named LibHunter, designed to against major code optimization strategies (e.g., Inlining and CallSite Optimization) while also resisting code obfuscation and shrinking. Extensive evaluations on a dataset of apps with optimization, obfuscation, and shrinking enabled show LibHunter significantly outperforms existing tools. It achieves F1 value that surpass the best tools by 29.3% and 36.1% at the library and version levels, respectively. We also applied LibHunter to detect vulnerable TPLs in the top Google Play apps, which shows the scalability of our approach, as well as the potential of our approach to facilitate malware detection.

The widespread use of mobile technology has led to the integration of mobile devices, especially smartwatches, into daily life due to their convenience and functionality. With Android being the most popular mobile operating system, Android-based smartwatches, such as those powered by Google’s Wear OS, have become increasingly popular. However, the customization of smartwatch firmware by manufacturers, while improving user experience, poses significant security risks. This study conducts a comprehensive security analysis of Android smartwatch firmware, focusing on security configurations, patch management, and pre-installed applications. Through the analysis of 176 firmware images from 24 vendors, the study identifies 1,684 insecure configurations resulting from customization, significant delays in applying security patches, and reveals that 26.1% of pre-installed apps have potential security risks. These findings underscore security concerns in Android smartwatch firmware and highlight the need to prioritize security in firmware development and customization practices.

Private browsing is a common feature of web browsers on desktop platforms. This feature protects the privacy of users browsing the Internet and, therefore, is widely welcomed by users. In recent years, with the popularity of smartphones, the private browsing mode has been introduced into mobile browsers. However, its deployment on mobile platforms has not been well evaluated. To bridge the gap, in this work, we systemically studied the private browsing modes of Android browser apps. Specifically, we proposed six private rules for mobile browsers to follow by combining the mobile browsing features with the previous research on private browsing. Furthermore, we designed an automated analysis framework, BroDroid, to detect whether mobile browsers violate these rules. Also, with BroDroid, we evaluated 49 popular browser apps crawled from Google Play. Finally, BroDroid successfully identified 58 violations, some of which come from the promised capabilities of the browser. We reported our discovered issues to the corresponding developers, and four of them (Yandex Browser, Mint Browser, Web Explorer, and Net Fast Web Browser) have acknowledged our findings. Our observation may be the tip of the iceberg, and more efforts should be put into improving the privacy protections of mobile browsers.

IoT devices have brought invaluable convenience to our daily life. However, their pervasiveness also amplifies the impact of security vulnerabilities. Many widespread vulnerabilities of embedded systems reside in their vulnerable border services. Unfortunately, existing vulnerability detection methods can neither effectively nor efficiently analyze such border services: they either introduce heavy execution overheads or have many false positives and negatives. In this paper, we propose a novel static taint checking solution, SaTC, to effectively detect security vulnerabilities in border services provided by embedded devices. Our key insight is that string literals on border interfaces are commonly shared between front-end files and back-end binaries to encode user input. Thus, we extract common keywords from the front-end and use them to locate reference points in the back-end, which indicate the input entry. Then, we apply targeted data-flow analysis to detect dangerous uses of the untrusted user input accurately. We implemented a prototype of SaTC and evaluated it on 39 firmware samples from six popular vendors. SaTC discovered 36 unknown bugs, of which CVE/CNVD/PSV confirms 33. Compared to the state-of-the-art tool KARONTE, SaTC found significantly more bugs in the test set. It shows that SaTC is effective in discovering bugs in embedded systems.

The popularity of Android OS is largely credited to massive apps, and many app developers are involved in this ecosystem. On the other hand, various vulnerabilities are introduced into apps by developers carelessly, bringing security issues to users. To facilitate secure development and avoid common API misuses, Google provides a series of security guidelines and development practices for developers on the official developer community websites. However, the deployments of these guidelines in the wild have not been systematically evaluated. In this work, through large-scale app measurement (251,749 apps from 10 markets) and analysis, we investigated whether app developers follow the official Android security guidelines and the possible reasons behind it. In practice, we selected five guidelines related to app data security as representatives, covering: (1) secure file creation modes; (2) sensitive data storage; (3) validation check for file paths; (4) hardware ID usage; (5) custom permission protection. We also designed the corresponding detection strategies to check violations of the guidelines. The results show that most developers (> 90 %) can comply with Guidelines 1 and 2. However, some guidelines have not been followed properly. For Guidelines 3, 4, and 5, less than 60 % of developers followed the Google security suggestions.

Android is the most popular smartphone platform with over 85% market share. Its success is built on openness, and phone vendors can utilize the Android source code to make customized products with unique software/hardware features. On the other hand, the fragmentation and customization of Android also bring many security risks that have attracted the attention of researchers. Many efforts were put in to investigate the security of customized Android firmware. However, most of the previous works focus on designing efficient analysis tools or analyzing particular aspects of the firmware. There still lacks a panoramic view of Android firmware ecosystem security and the corresponding understandings based on large-scale firmware datasets. In this work, we made a large-scale comprehensive measurement of the Android firmware ecosystem security. Our study is based on 8,325 firmware images from 153 vendors and 813 Android-related CVEs, which is the largest Android firmware dataset ever used for security measurements. In particular, our study followed a series of research questions, covering vulnerabilities, patches, security updates, and pre-installed apps. To automate the analysis process, we designed a framework,AndScanner+, to complete firmware crawling, firmware parsing, patch analysis, and app analysis. Through massive data analysis and case explorations, several interesting findings are obtained. For example, the patch delay and missing issues are widespread in Android firmware images, say 31.4% and 5.6% of all images, respectively. The latest images of several phones still contain vulnerable pre-installed apps, and even the corresponding vulnerabilities have been publicly disclosed. In addition to data measurements, we also explore the causes behind these security threats through case studies and demonstrate that the discovered security threats can be converted into exploitable vulnerabilities. There are 46 new vulnerabilities found byAndScanner+, 36 of which have been assigned CVE/CNVD IDs. This study provides much new knowledge of the Android firmware ecosystem with a deep understanding of software engineering security practices.

MQTT is widely adopted by IoT devices because it allows for the most efficient data transfer over a variety of communication lines. The security of MQTT has received increasing attention in recent years, and several studies have demonstrated the configurations of many MQTT brokers are insecure. Adversaries are allowed to exploit vulnerable brokers and publish malicious messages to subscribers. However, little has been done to understanding the security issues on the device side when devices handle unauthorized MQTT messages. To fill this research gap, we propose a fuzzing framework named ShadowFuzzer to find client-side vulnerabilities when processing incoming MQTT messages. To avoiding ethical issues, ShadowFuzzer redirects traffic destined for the actual broker to a shadow broker under the control to monitor vulnerabilities. We select 15 IoT devices communicating with vulnerable brokers and leverage ShadowFuzzer to find vulnerabilities when they parse MQTT messages. For these devices, ShadowFuzzer reports 34 zero-day vulnerabilities in 11 devices. We evaluated the exploitability of these vulnerabilities and received a total of 44,000 USD bug bounty rewards. And 16 CVE/CNVD/CN-NVD numbers have been assigned to us.

Android is the most popular smartphone platform with over 85% market share. Its success is built on openness, and phone vendors can utilize the Android source code to make products with unique software/hardware features. On the other hand, the fragmentation and customization of Android also bring many security risks that have attracted the attention of researchers. Many efforts were put in to investigate the security of customized Android firmware. However, most of the previous work focuses on designing efficient analysis tools or analyzing particular aspects of the firmware. There still lacks a panoramic view of Android firmware ecosystem security and the corresponding understandings based on large-scale firmware datasets. In this work, we made a large-scale comprehensive measurement of the Android firmware ecosystem security. Our study is based on 6,261 firmware images from 153 vendors and 602 Android-related CVEs, which is the largest Android firmware dataset ever used for security measurements. In particular, our study followed a series of research questions, covering vulnerabilities, patches, security updates, and pre-installed apps. To automate the analysis process, we designed a framework, AndScanner, to complete ROM crawling, ROM parsing, patch analysis, and app analysis. Through massive data analysis and case explorations, several interesting findings are obtained. For example, the patch delay and missing issues are widespread in Android images, say 24.2% and 6.1% of all images, respectively. The latest images of several phones still contain vulnerable pre-installed apps, and even the corresponding vulnerabilities have been publicly disclosed. In addition to data measurements, we also explore the causes behind these security threats through case studies and demonstrate that the discovered security threats can be converted into exploitable vulnerabilities via 38 newfound vulnerabilities by our framework, 32 of which have been assigned CVE/CNVD numbers. This study provides much new knowledge of the Android firmware ecosystem with deep understanding of software engineering security practices.

Android is a Linux-based multi-thread open-source operating system that dominates 85% of the worldwide smartphone market share. Though Android has its established management for its framework layer processes, we discovered for the first time that the weak management of native processes is posing tangible threats to Android systems from version 4.2 to 9.0. As a consequence, any third-party application without any permission can freeze the system or force the system to go through a reboot by starving or significantly delaying the critical system services using Android commands in its native processes. We design NativeX to systematically analyze the Android source code to identify the risky Android commands. For each identified risky command, NativeX can automatically generate the PoC (Proof-of-Concept) application, and verify the effectiveness of the generated PoC. We conduct manual vulnerability analysis to reveal two root causes beyond the superficial attack consequences. We further carry out quantitative experiments to demonstrate the attack consequences, including the device temperature surge, the battery degeneration, and the computing performance decrease, based on which, three representative PoC attacks are engineered. Finally, we discuss possible defense approaches to improve the management of Android native processes.