arXiv subjects

Wei Minn

Publications and source records attributed to Wei Minn.

6 recordsLinked to original sources

arXiv2026

Beyond the Tip of the Iceberg: Understanding SATD in Dockerfiles through the Lens of Co-evolution

Dockerfiles enable the creation of portable container-based execution environments for the application code, and have become an important part of the modern software development process. As Dockerfiles are a form of Infrastructure-as-Code (IaC), they can include temporary workarounds and other suboptimal implementations, leading to the accrual of technical debt that affects their reliability, security, and maintainability in the future. Prior work characterized self-admitted technical debt (SATD) in Dockerfile comments and the surrounding file chunks. This single-file view is incomplete since source code evolution involves changes across different types of software artifacts such as production, test, build, and other configuration files. Thus, we address this gap by studying SATD events in Dockerfiles alongside the related source code. We find that approximately 27% of admission events and 40% of repayment events are coupled to non-Dockerfile artifacts, and coupling sources are subtype-specific. We also observed that coupled SATD in general are repaid significantly faster overall (p = 0.0201), while coupled SATD regarding missing functionalities persists longer than its isolated counterparts; Lastly, we conducted open and axial coding of coupled SATD events, and we observe that external dependency issues, more particularly regarding unreleased upstream packages and bug fixes, are the most common cause of admission triggers in the source code; we also observe that architectural refactoring is the most common prerequisite for the repayment of SATD in Dockerfiles. These findings indicate that both practitioners (e.g. developers and project managers) and SATD researchers should integrate the source code-side co-evolution, rather than the single-file view, as the primary unit of analysis.

cs.SE↗

arXiv2026

CovAgent: Overcoming the 30% Curse of Mobile Application Coverage with Agentic AI and Dynamic Instrumentation

Automated GUI testing is crucial for ensuring the quality and reliability of Android apps. However, the efficacy of existing UI testing techniques is often limited, especially in terms of coverage. Recent studies, including the state-of-the-art, struggle to achieve more than 30% activity coverage in real-world apps. This limited coverage can be attributed to a combination of factors such as failing to generate complex user inputs, unsatisfied activation conditions regarding device configurations and external resources, and hard-to-reach code paths that are not easily accessible through the GUI. To overcome these limitations, we propose CovAgent, a novel agentic AI-powered approach to enhance Android app UI testing. Our fuzzer-agnostic framework comprises an AI agent that inspects the app's decompiled Smali code and component transition graph, and reasons about unsatisfied activation conditions within the app code logic that prevent access to the activities that are unreachable by standard and widely adopted GUI fuzzers. Then, another agent generates dynamic instrumentation scripts that satisfy activation conditions required for successful transitions to those activities. We found that augmenting existing fuzzing approaches with our framework achieves a significant improvement in test coverage over the state-of-the-art, LLMDroid, and other baselines such as Fastbot and APE (e.g., 101.1%, 116.3% and 179.7% higher activity coverage, respectively). CovAgent also outperforms all the baselines in other metrics such as class, method, and line coverage. We also conduct investigations into components within CovAgent to reveal further insights regarding the efficacy of Agentic AI in the field of automated app testing such as the agentic activation condition inference accuracy, and agentic activity-launching success rate.

cs.SE↗

arXiv2026

Virtualization-based Penetration Testing Study for Detecting Accessibility Abuse Vulnerabilities in Banking Apps in East and Southeast Asia

Android banking applications have revolutionized financial management by allowing users to perform various financial activities through mobile devices. However, this convenience has attracted cybercriminals who exploit security vulnerabilities to access sensitive financial data. FjordPhantom, a malware identified by our industry collaborator, uses virtualization and hooking to bypass the detection of malicious accessibility services, allowing it to conduct keylogging, screen scraping, and unauthorized data access. This malware primarily affects banking and finance apps across East and Southeast Asia region where our industry partner's clients are primarily based in. It requires users to be deceived into installing a secondary malicious component and activating a malicious accessibility service. In our study, we conducted an empirical study on the susceptibility of banking apps in the region to FjordPhantom, analyzed the effectiveness of protective measures currently implemented in those apps, and discussed ways to detect and prevent such attacks by identifying and mitigating the vulnerabilities exploited by this malware.

cs.CR↗

arXiv2025

Bamboo: LLM-Driven Discovery of API-Permission Mappings in the Android Framework

The permission mechanism in the Android Framework is integral to safeguarding the privacy of users by managing users' and processes' access to sensitive resources and operations. As such, developers need to be equipped with an in-depth understanding of API permissions to build robust Android apps. Unfortunately, the official API documentation by Android chronically suffers from imprecision and incompleteness, causing developers to spend significant effort to accurately discern necessary permissions. This potentially leads to incorrect permission declarations in Android app development, potentially resulting in security violations and app failures. Recent efforts in improving permission specification primarily leverage static and dynamic code analyses to uncover API-permission mappings within the Android framework. Yet, these methodologies encounter substantial shortcomings, including poor adaptability to Android SDK and Framework updates, restricted code coverage, and a propensity to overlook essential API-permission mappings in intricate codebases. This paper introduces a pioneering approach utilizing large language models (LLMs) for a systematic examination of API-permission mappings. In addition to employing LLMs, we integrate a dual-role prompting strategy and an API-driven code generation approach into our mapping discovery pipeline, resulting in the development of the corresponding tool, \tool{}. We formulate three research questions to evaluate the efficacy of \tool{} against state-of-the-art baselines, assess the completeness of official SDK documentation, and analyze the evolution of permission-required APIs across different SDK releases. Our experimental results reveal that \tool{} identifies 2,234, 3,552, and 4,576 API-permission mappings in Android versions 6, 7, and 10 respectively, substantially outprforming existing baselines.

cs.SE↗

arXiv2025

Runtime Anomaly Detection for Drones: An Integrated Rule-Mining and Unsupervised-Learning Approach

UAVs, commonly referred to as drones, have witnessed a remarkable surge in popularity due to their versatile applications. These cyber-physical systems depend on multiple sensor inputs, such as cameras, GPS receivers, accelerometers, and gyroscopes, with faults potentially leading to physical instability and serious safety concerns. To mitigate such risks, anomaly detection has emerged as a crucial safeguarding mechanism, capable of identifying the physical manifestations of emerging issues and allowing operators to take preemptive action at runtime. Recent anomaly detection methods based on LSTM neural networks have shown promising results, but three challenges persist: the need for models that can generalise across the diverse mission profiles of drones; the need for interpretability, enabling operators to understand the nature of detected problems; and the need for capturing domain knowledge that is difficult to infer solely from log data. Motivated by these challenges, this paper introduces RADD, an integrated approach to anomaly detection in drones that combines rule mining and unsupervised learning. In particular, we leverage rules (or invariants) to capture expected relationships between sensors and actuators during missions, and utilise unsupervised learning techniques to cover more subtle relationships that the rules may have missed. We implement this approach using the ArduPilot drone software in the Gazebo simulator, utilising 44 rules derived across the main phases of drone missions, in conjunction with an ensemble of five unsupervised learning models. We find that our integrated approach successfully detects 93.84% of anomalies over six types of faults with a low false positive rate (2.33%), and can be deployed effectively at runtime. Furthermore, RADD outperforms a state-of-the-art LSTM-based method in detecting the different types of faults evaluated in our study.

cs.SE↗

arXiv2025

Shelving it rather than Ditching it: Dynamically Debloating DEX and Native Methods of Android Applications without APK Modification

Today's Android developers tend to include numerous features to accommodate diverse user requirements, which inevitably leads to bloated apps. Yet more often than not, only a fraction of these features are frequently utilized by users, thus a bloated app costs dearly in potential vulnerabilities, expanded attack surfaces, and additional resource consumption. Especially in the event of severe security incidents, users have the need to block vulnerable functionalities immediately. Existing works have proposed various code debloating approaches for identifying and removing features of executable components. However, they typically involve static modification of files (and, for Android apps, repackaging of APKs, too), which lacks user convenience let alone undermining the security model of Android due to the compromising of public key verification and code integrity checks. This paper introduces 3DNDroid, a Dynamic Debloating approach targeting both DEX and Native methods in AnDroid apps. Using an unprivileged management app in tandem with a customized Android OS, 3DNDroid dynamically reduces unnecessary code loading during app execution based on a pre-generated debloating schema from static or dynamic analyses. It intercepts invocations of debloated bytecode methods to prevent their interpretation, compilation, and execution, while zero-filling memory spaces of debloated native methods during code loading. Evaluation demonstrates 3DNDroid's ability to debloat 187 DEX methods and 30 native methods across 55 real-world apps, removing over 10K Return-Oriented Programming (ROP) gadgets. Case studies confirm its effectiveness in mitigating vulnerabilities, and performance assessments highlight its resource-saving advantages over non-debloated apps.

cs.CR↗