Security assessment of open source third-parties applications

Free and Open Source Software (FOSS) components are ubiquitous in both proprietary and open source applications. In this dissertation we discuss challenges that large software vendors face when they must integrate and maintain FOSS components into their software supply chain. Each time a vulnerability is disclosed in a FOSS component, a software vendor must decide whether to update the component, patch the application itself, or just do nothing as the vulnerability is not applicable to the deployed version that may be old enough to be not vulnerable. This is particularly challenging for enterprise software vendors that consume thousands of FOSS components, and offer more than a decade of support and security fixes for applications that include these components. First, we design a framework for performing security vulnerability experimentations. In particular, for testing known exploits for publicly disclosed vulnerabilities against different versions and software configurations. Second, we provide an automatic screening test for quickly identifying the versions of FOSS components likely affected by newly disclosed vulnerabilities: a novel method that scans across the entire repository of a FOSS component in a matter of minutes. We show that our screening test scales to large open source projects. Finally, for facilitating the global security maintenance of a large portfolio of FOSS components, we discuss various characteristics of FOSS components and their potential impact on the security maintenance effort, and empirically identify the key drivers

An automatic method for assessing the versions affected by a vulnerability

Vulnerability data sources are used by academics to build models, and by industry and government to assess compliance. Errors in such data sources therefore not only are threats to validity in scientific studies, but also might cause organizations, which rely on retro versions of software, to lose compliance. In this work, we propose an automated method to determine the code evidence for the presence of vulnerabilities in retro software versions. The method scans the code base of each retro version of software for the code evidence to determine whether a retro version is vulnerable or not. It identifies the lines of code that were changed to fix vulnerabilities. If an earlier version contains these deleted lines, it is highly likely that this version is vulnerable. To show the scalability of the method we performed a large scale experiments on Chrome and Firefox (spanning 7,236 vulnerable files and approximately 9,800 vulnerabilities) on the National Vulnerability Database (NVD). The elimination of spurious vulnerability claims (e.g. entries to a vulnerability database such as NVD) found by our method may change the conclusions of studies on the prevalence of foundational vulnerabilities

Dissecting Android Cryptocurrency Miners

Cryptojacking applications pose a serious threat to mobile devices. Due to the extensive computations, they deplete the battery fast and can even damage the device. In this work we make a step towards combating this threat. We collected and manually verified a large dataset of Android mining apps. In this paper, we analyze the gathered miners and identify how they work, what are the most popular libraries and APIs used to facilitate their development, and what static features are typical for this class of applications. Further, we analyzed our dataset using VirusTotal. The majority of our samples is considered malicious by at least one VirusTotal scanner, but 16 apps are not detected by any engine; and at least 5 apks were not seen previously by the service. Mining code could be obfuscated or fetched at runtime, and there are many confusing miner-related apps that actually do not mine. Thus, static features alone are not sufficient for miner detection.We have collected a feature set of dynamic metrics both for miners and unrelated benign apps, and built a machine learning-based tool for dynamic detection. Our BrenntDroid tool is able to detect miners with 95% of accuracy on our dataset

Dissecting Android Cryptocurrency Miners

Cryptojacking applications pose a serious threat to mobile devices. Due to the extensive computations, they deplete the battery fast and can even damage the device. In this work we make a step towards combating this threat. We collected and manually verified a large dataset of Android mining apps. In this paper, we analyze the gathered miners and identify how they work, what are the most popular libraries and APIs used to facilitate their development, and what static features are typical for this class of applications. Further, we analyzed our dataset using VirusTotal. The majority of our samples is considered malicious by at least one VirusTotal scanner, but 16 apps are not detected by any engine; and at least 5 apks were not seen previously by the service. Mining code could be obfuscated or fetched at runtime, and there are many confusing miner-related apps that actually do not mine. Thus, static features alone are not sufficient for miner detection.We have collected a feature set of dynamic metrics both for miners and unrelated benign apps, and built a machine learning-based tool for dynamic detection. Our BrenntDroid tool is able to detect miners with 95% of accuracy on our dataset

Fine-grained Code Coverage Measurement in Automated Black-box Android Testing

Today, there are millions of third-party Android applications. Some of them are buggy or even malicious. To identify such applications, novel frameworks for automated black-box testing and dynamic analysis are being developed by the Android community. Code coverage is one of the most common metrics for evaluating effectiveness of these frameworks. Furthermore, code coverage is used as a fitness function for guiding evolutionary and fuzzy testing techniques. However, there are no reliable tools for measuring fine-grained code coverage in black-box Android app testing. We present the Android Code coVerage Tool, ACVTool for short, that instruments Android apps and measures code coverage in the black-box setting at class, method and instruction granularity. ACVTool has successfully instrumented 96.9% of apps in our experiments. It introduces a negligible instrumentation time overhead, and its runtime overhead is acceptable for automated testing tools. We demonstrate practical value of ACVTool in a large-scale experiment with Sapienz, a state-of-art automated testing tool. Using ACVTool on the same cohort of apps, we have compared different coverage granularities applied by Sapienz in terms of the found amount of crashes. Our results show that none of the applied coverage granularities clearly outperforms others in this aspect