Abstract Codes of Open Source Software (OSS) are widely reused during software development nowadays. However, reusing some specific versions of OSS introduces 1-day vulnerabilities of which details are publicly available, which may be exploited and lead to serious security issues. Existing state-of-the-art OSS reuse detection work can not identify the specific versions of reused OSS well. The features they selected are not distinguishable enough for version detection and the matching scores are only based on similarity.This paper presents B2SMatcher, a fine-grained version identification tool for OSS in commercial off-the-shelf (COTS) software. We first discuss five kinds of version-sensitive code features that are trackable in both binary and source code. We categorize these features into program-level features and function-level features and propose a two-stage version identification approach based on the two levels of code features. B2SMatcher also identifies different types of OSS version reuse based on matching scores and matched feature instances. In order to extract source code features as accurately as possible, B2SMatcher innovatively uses machine learning methods to obtain the source files involved in the compilation and uses function abstraction and normalization methods to eliminate the comparison costs on redundant functions across versions. We have evaluated B2SMatcher using 6351 candidate OSS versions and 585 binaries. The result shows that B2SMatcher achieves a high precision up to 89.2% and outperforms state-of-the-art tools. Finally, we show how B2SMatcher can be used to evaluate real-world software and find some security risks in practice.

COTS software products are developed extensively on top of OSS projects, resulting in OSS reuse vulnerabilities. To detect such vulnerabilities, finding OSS reuses in COTS software has become imperative. While scalable to tens of thousands of OSS projects, existing binary-to-source matching approaches are severely imprecise in analyzing COTS software products, since they support only a limited number of code features, compute matching scores only approximately in measuring OSS reuses, and neglect the code structures in OSS projects. We introduce a novel binary-to-source matching approach, called B2SFINDER1, to address these limitations. First of all, B2SFINDER can reason about seven kinds of code features that are traceable in both binary and source code. In order to compute matching scores precisely, B2SFINDER employs a weighted feature matching algorithm that combines three matching methods (for dealing with different code features) with two importance-weighting methods (for computing the weight of an instance of a code feature in a given COTS software application based on its specificity and occurrence frequency). Finally, B2SFINDER identifies different types of code reuses based on matching scores and code structures of OSS projects. We have implemented B2SFINDER using an optimized data structure. We have evaluated B2SFINDER using 21991 binaries from 1000 popular COTS software products and 2189 candidate OSS projects. Our experimental results show that B2SFINDER is not only precise but also scalable. Compared with the state ofthe art, B2SFINDER has successfully found up to 2.15× as many reuse cases in 53.85 seconds per binary file on average. We also discuss how B2SFINDER can be leveraged in detecting OSS reuse vulnerabilities in practice.

Open-source licenses are widely used in open-source projects. However, developers using or modifying the source code of open-source projects do not always strictly follow the licenses. GPL and AGPL, two of the most popular copyleft licenses, are most likely to be violated, because they require developers to open-source the entire project if any code under GPL/AGPL protection is included whether modified or not. There are few license violation detectors focusing on binary software, owning to the challenge of mapping binary code to source code efficiently and accurately at large scale. In this paper, we propose a scalable and fully-automated system to check open-source license violation of binary software at large scale. We match source code to binary code by analyzing file attributes of executable files and code features that are not affected by compilation and could vary between projects. Moreover, to break the barrier of large-scale analysis, we introduce an automatic extractor to parse executable files from installation packages that are broadly available in software download sites. In empirical experiments of binary-to-source mapping, we have got a remarkable high accuracy of 99.5% and recall of 95.6% without significant loss of precision. Besides, 2270 pairs of binary-to-source mapping relationships are discovered, with 110 license violations of GPL and AGPL licenses related to 7.2% of the 1000 real-world binary software projects.