Detecting security vulnerabilities in backend Web applications as well as mobile apps is extremely important. Static analysis for vulnerability analysis has subsequently developed as an important field of research. Researchers need extensible frameworks to avoid starting from scratch with every new research project. Compared to commercially available scanners, open-source frameworks often only provide basic functionality. This limits the ability of researchers to evaluate novel algorithms. Lacking access to full code scanners, new building blocks are often tested in isolation. In this paper, we present VUSC, a fast, precise and extensible vulnerability scanner for Android and Java bytecode. It features a plugin architecture for commonly used static analyses such as call graph, taint and value analyses, allowing researchers to build upon our work and using VUSC as a reference platform. We show that VUSC achieves a precision of around 90% on benchmarks. Video: https://youtu.be/QpXs9hv5zGc, Dataset: https://github.com/Fraunhofer-SIT/ASE2025-StaticAnalysisInfrastructure/

When fuzzing a proprietary file-processing program, one typically executes the whole program repeatedly with sampled input files, and distinguishes between normal and abnormal termination. While this works well for many command-line utilities, it is more complicated for programs that usually do not terminate after input file processing. Many real-world applications are examples of such programs, in particular, those with a graphical user interface (GUI), such as image editors, media players and document viewers. In these cases, the fuzzer has to define the scope of the execution and forcefully terminate the program under test.In order to efficiently fuzz test file-processing programs with a GUI, a standard approach is to define a dedicated testing harness, which executes the file processing in isolation and strips irrelevant program parts. However, this either requires the source code of the program or an expert’s effort in reverse engineering. Alternative approaches work on the unmodified binary of the program, and use a heuristic to decide when the input processing is likely done. For example, one can terminate the program after a fixed timeout or once its CPU usage has dropped below a threshold. We show that these heuristics, while simple to implement, are inefficient and ineffective.We present Terminator, a fully-automated approach to facilitate efficient fuzzing of closed-source file-processing programs with a GUI. Terminator modifies the binary of the program under test so that it automatically terminates when code coverage stops increasing without user interaction. Consequently, Terminator (1) ensures that the program terminates soon after the input processing instead of waiting for user interaction, and, at the same time, (2) prevents premature termination during input processing. We show that Terminator outperforms the timeout and CPU usage heuristics and significantly increases fuzzing efficiency.

Millions of users nowadays rely on their smartphones to process sensitive data through apps from various vendors and sources. Therefore, it is vital to assess these apps for security vulnerabilities and privacy violations. Information such as to which server an app connects through which protocol, and which algorithm it applies for encryption, are usually encoded as variable values and arguments of API calls. However, extracting these values from an app is not trivial. The source code of an app is usually not available, and manual reverse engineering is cumbersome with binary sizes in the tens of megabytes. Current automated tools, however, cannot retrieve values that are computed at runtime through complex transformations. In this article, we present ValDroid , a novel static analysis tool for automatically extracting the set of possible values for a given variable at a given statement in the Dalvik byte code of an Android app. We evaluate ValDroid against existing approaches (JSA, Violist, DroidRA, Harvester, BlueSeal, StringHound, IC3, and COAL) on benchmarks and 794 real-world apps. ValDroid greatly outperforms existing tools. It provides an average F 1 score of more than 90%, while only requiring 0.1 s per value on average. For many data types including Network Connections and Dynamic Code Loading, its recall is more than twice the recall of the best existing approaches.

Over the years, security researchers have developed a broad spectrum of automatic code scanners that aim to find security vulnerabilities in applications. Security benchmarks are commonly used to evaluate novel scanners or program analysis techniques. Each benchmark consists of multiple positive test cases that reflect typical implementations of vulnerabilities, as well as negative test cases, that reflect secure implementations without security flaws. Based on this ground truth, researchers can demonstrate the recall and precision of their novel contributions.

Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy-related concerns. Recent regulations like the European GDPR provide rules for the processing of personal and sensitive data, like that no such data may be leaked without the consent of the user.Researchers have proposed sophisticated approaches to track sensitive data within mobile apps, all of which rely on specific lists of sensitive SOURCE and SINK API methods. The data flow analysis results greatly depend on these lists’ quality. Previous approaches either used incomplete hand-written lists that quickly became outdated or relied on machine learning. The latter, however, leads to numerous false positives, as we show.This paper introduces CoDoC, a tool that aims to revive the machine-learning approach to precisely identify privacy-related SOURCE and SINK API methods. In contrast to previous approaches, CoDoC uses deep learning techniques and combines the source code with the documentation of API methods. Firstly, we propose novel definitions that clarify the concepts of sensitive SOURCE and SINK methods. Secondly, based on these definitions, we build a new ground truth of Android methods representing sensitive SOURCE, SINK, and NEITHER (i.e., no source or sink) methods that will be used to train our classifier.We evaluate CoDoC and show that, on our validation dataset, it achieves a precision, recall, and F1score of 91% in 10-fold cross-validation, outperforming the state-of-the-art SUSI when used on the same dataset. However, similarly to existing tools, we show that in the wild, i.e., with unseen data, CoDoC performs poorly and generates many false positive results. Our findings, together with time-tested results of previous approaches, suggest that machine-learning models for abstract concepts such as privacy fail in practice despite good lab results. To encourage future research, we release all our artifacts to the community.

Users increasingly rely on mobile apps for everyday tasks, including security- and privacy-sensitive tasks such as online banking, e-health, and e-government. Additionally, a wealth of sensors captures the movements and habits of the users for fitness tracking and convenience. Despite legal regulations imposing requirements and limits on the processing of privacy-sensitive data, users must still trust the app developers to apply suffcient protections. In this paper, we investigate the state of security in Android apps and how security-related code smells have evolved since the introduction of the Android operating system.

Static data flow analysis is an integral building block for many applications, ranging from compile-time code optimization to security and privacy analysis. When assessing whether a mobile app is trustworthy, for example, analysts need to identify which of the user's personal data is sent to external parties such as the app developer or cloud providers. Since accessing and sending data is usually done via API calls, tracking the data flow between source and sink API is often the method of choice. Precise algorithms such as IFDS help reduce the number of false positives, but also introduce significant performance penalties. With its fixpoint iteration over the program's entire exploded supergraph, IFDS is particularly memory-intensive, consuming hundreds of megabytes or even several gigabytes for medium-sized apps. In this paper, we present a technique called CleanDroid for reducing the memory footprint of a precise IFDS-based data flow analysis and demonstrate its effectiveness in the popular FlowDroid open-source data flow solver. CleanDroid efficiently removes edges from the path edge table used for the IFDS fixpoint iteration without affecting termination. As we show on 600 realworld Android apps from the Google Play Store, CleanDroid reduces the average per-app memory consumption by around 63% to 78%. At the same time, CleanDroid speeds up the analysis by up to 66%.

Today’s software systems are created by software development processes that naturally include mistakes, some of which can be exploited by attackers and are therefore called vulnerabilities. Automatic software scanners enable developers to analyze their applications to detect vulnerabilities and alert them of their presence. But often these reports are hard to understand, include false positives or overwhelm users due to the sheer number of alerts, since a report may contain hundreds to thousands of vulnerabilities. Developers must undergo a process called vulnerability triage to find the relevant vulnerabilities to fix. This paper presents two interactive visualizations for developers and security experts to gain an overview of the security state of their application. Users can see the distribution of vulnerabilities, find the most relevant ones, and compare differences between application versions. Our visualization design is inspired by an initial preliminary study and has been evaluated by domain experts to investigate the usability and appropriateness.

Android applications, or apps, provide useful features to end-users, but many apps also contain malicious behavior. Modern malware makes understanding such behavior challenging by behaving maliciously only under particular conditions. For example, a malware app may check whether it runs on a real device and not an emulator, in a particular country, and alongside a specific target app, such as a vulnerable banking app. To observe the malicious behavior, a security analyst must find out and emulate all these app-specific constraints. This paper presents FuzzDroid, a framework for automatically generating an Android execution environment where an app exposes its malicious behavior. The key idea is to combine an extensible set of static and dynamic analyses through a search-based algorithm that steers the app toward a configurable target location. On recent malware, the approach reaches the target location in 75% of the apps. In total, we reach 240 code locations within an average time of only one minute. To reach these code locations, FuzzDroid generates 106 different environments, too many for a human analyst to create manually.

Smartphone users suffer from insufficient information on how commercial as well as malicious apps handle sensitive data stored on their phones. Automated taint analyses address this problem by allowing users to detect and investigate how applications access and handle this data. A current problem with virtually all those analysis approaches is, though, that they rely on explicit models of the Android runtime library. In most cases, the existence of those models is taken for granted, despite the fact that the models are hard to come by: Given the size and evolution speed of a modern smartphone operating system it is prohibitively expensive to derive models manually from code or documentation.

Shake Them All is a popular "Wallpaper" application exceeding millions of downloads on Google Play. At installation, this application is given permission to (1) access the Internet (for updating wallpapers) and (2) use the device microphone (to change background following noise changes). With these permissions, the application could silently record user conversations and upload them remotely. To give more confidence about how Shake Them All actually processes what it records, it is necessary to build a precise analysis tool that tracks the flow of any sensitive data from its source point to any sink, especially if those are in different components. Since Android applications may leak private data carelessly or maliciously, we propose IccTA, a static taint analyzer to detect privacy leaks among components in Android applications. IccTA goes beyond state-of-the-art approaches by supporting inter- component detection. By propagating context information among components, IccTA improves the precision of the analysis. IccTA outperforms existing tools on two benchmarks for ICC-leak detectors: DroidBench and ICC-Bench. Moreover, our approach detects 534 ICC leaks in 108 apps from MalGenome and 2,395 ICC leaks in 337 apps in a set of 15,000 Google Play apps.

What is it that makes an app malicious? One important factor is that malicious apps treat sensitive data differently from benign apps. To capture such differences, we mined 2,866 benign Android applications for their data flow from sensitive sources, and compare these flows against those found in malicious apps. We find that (a) for every sensitive source, the data ends up in a small number of typical sinks; (b) these sinks differ considerably between benign and malicious apps; (c) these differences can be used to flag malicious apps due to their abnormal data flow; and (d) malicious apps can be identified by their abnormal data flow alone, without requiring known malware samples. In our evaluation, our MUDFLOW prototype correctly identified 86.4% of all novel malware, and 90.1% of novel malware leaking sensitive data.

Smartphones are nowadays used to store and process many kinds of privacy-sensitive data such as contacts, photos, and e-mails. Sensors provide access to the phone's physical location, and can record audio and video. While this is convenient for many applications, it also makes smartphones a worthwhile target for attackers providing malicious applications. Current approaches to runtime enforcement try to mitigate unauthorized leaks of confidential data. However, they are often capable of enforcing only a very limited set of policies, like preventing data leaks only within single components or monitoring access only to specific sensitive system resources. In this work, we present Droid Force, an approach for enforcing complex, data-centric, system-wide policies on Android applications. Droid Force allows users to specify fine-grained constraints on how and when which data may be processed on their phones, regardless of whether the malicious behavior is distributed over different colluding components or even applications. Policies can be dynamically exchanged at runtime and no modifications to the operating system nor root access to the phone are required. Droid Force works purely on the application level. It provides a centralized policy decision point as a dedicated Android application and it instruments a decentralized policy enforcement point into every target application. Analyzing and instrumenting an application takes in total less than a minute and secured applications exhibit no noticeable slowdown in practice.

Most application code evolves incrementally, and especially so when being maintained after the applications have been deployed. Yet, most data-flow analyses do not take advantage of this fact. Instead they require clients to recompute the entire analysis even if little code has changed—a time consuming undertaking, especially with large libraries or when running static analyses often, e.g., on a continuous-integration server. In this work, we present Reviser, a novel approach for automatically and efficiently updating inter-procedural dataflow analysis results in response to incremental program changes. Reviser follows a clear-and-propagate philosophy, aiming at clearing and recomputing analysis information only where required, thereby greatly reducing the required computational effort. The Reviser algorithm is formulated as an extension to the IDE framework for Inter-procedural Finite Distributed Environment problems and automatically updates arbitrary IDE-based analyses. We have implemented Reviser as an open-source extension to the Heros IFDS/IDE solver and the Soot program-analysis framework. An evaluation of Reviser on various client analyses and target programs shows performance gains of up to 80% in comparison to a full recomputation. The experiments also show Reviser to compute the same results as a full recomputation on all instances tested.

Today's smartphones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by carelessly programmed apps that leak important data by accident, and by malicious apps that exploit their given privileges to copy such data intentionally. While existing static taint-analysis approaches have the potential of detecting such data leaks ahead of time, all approaches for Android use a number of coarse-grain approximations that can yield high numbers of missed leaks and false alarms.