No abstract

The lack of reliable sources of detailed information on the vulnerabilities of open-source software (OSS) components is a major obstacle to maintaining a secure software supply chain and an effective vulnerability management process. Standard sources of advisories and vulnerability data, such as the National Vulnerability Database (NVD), are known to suffer from poor coverage and inconsistent quality. To reduce our dependency on these sources, we propose an approach that uses machine-learning to analyze source code repositories and to automatically identify commits that are security-relevant (i.e., that are likely to fix a vulnerability). We treat the source code changes introduced by commits as documents written in natural language, classifying them using standard document classification methods. Combining independent classifiers that use information from different facets of commits, our method can yield high precision (80%) while ensuring acceptable recall (43%). In particular, the use of information extracted from the source code changes yields a substantial improvement over the best known approach in state of the art, while requiring a significantly smaller amount of training data and employing a simpler architecture.

Advancing our understanding of software vulnerabilities, automating their identification, the analysis of their impact, and ultimately their mitigation is necessary to enable the development of software that is more secure. While operating a vulnerability assessment tool that we developed and that is currently used by hundreds of development units at SAP, we manually collected and curated a dataset of vulnerabilities of open-source software and the commits fixing them. The data was obtained both from the National Vulnerability Database (NVD) and from project-specific Web resources that we monitor on a continuous basis. From that data, we extracted a dataset that maps 624 publicly disclosed vulnerabilities affecting 205 distinct open-source Java projects, used in SAP products or internal tools, onto the 1282 commits that fix them. Out of 624 vulnerabilities, 29 do not have a CVE identifier at all and 46, which do have a CVE identifier assigned by a numbering authority, are not available in the NVD yet. The dataset is released under an open-source license, together with supporting scripts that allow researchers to automatically retrieve the actual content of the commits from the corresponding repositories and to augment the attributes available for each instance. Also, these scripts allow to complement the dataset with additional instances that are not security fixes (which is useful, for example, in machine learning applications). Our dataset has been successfully used to train classifiers that could automatically identify security-relevant commits in code repositories. The release of this dataset and the supporting code as open-source will allow future research to be based on data of industrial relevance; also, it represents a concrete step towards making the maintenance of this dataset a shared effort involving open-source communities, academia, and the industry.

The use of open-source software (OSS) is ever-increasing, and so is the number of open-source vulnerabilities being discovered and publicly disclosed. The gains obtained from the reuse of community-developed libraries may be offset by the cost of timely detecting, assessing, and mitigating their vulnerabilities. In this paper we present a novel method to detect, assess and mitigate OSS vulnerabilities that improves on state-of-the-art approaches, which commonly depend on metadata to identify vulnerable OSS dependencies. Our solution instead is code-centric and combines static and dynamic analysis to determine the reachability of the vulnerable portion of libraries used (directly or transitively) by an application. Taking this usage into account, our approach then supports developers in choosing among the existing non-vulnerable library versions. Vulas, the tool implementing our code-centric and usage-based approach, is officially recommended by SAP to scan its Java software, and has been successfully used to perform more than 250000 scans of about 500 applications since December 2016. We report on our experience and on the lessons we learned when maturing the tool from a research prototype to an industrial-grade solution.

oftware applications integrate more and more open-source software (OSS) to benefit from code reuse. As a drawback, each vulnerability discovered in bundled OSS potentially affects the application. Upon the disclosure of every new vulnerability, the application vendor has to decide whether it is exploitable in his particular usage context, hence, whether users require an urgent application patch containing a non-vulnerable version of the OSS. Current decision making is mostly based on high-level vulnerability descriptions and expert knowledge, thus, effort intense and error prone. This paper proposes a pragmatic approach to facilitate the impact assessment, describes a proof-of-concept for Java, and examines one example vulnerability as case study. The approach is independent from specific kinds of vulnerabilities or programming languages and can deliver immediate results.