Anomalicious: Automated Detection of Anomalous and Potentially Malicious Commits on GitHub

Abstract

Security is critical to the adoption of open source software (OSS), yet few automated solutions currently exist to help detect and prevent malicious contributions from infecting open source repositories. On GitHub, a primary host of OSS, repositories contain not only code but also a wealth of commit-related and contextual metadata - what if this metadata could be used to automatically identify malicious OSS contributions? In this work, we show how to use only commit logs and repository metadata to automatically detect anomalous and potentially malicious commits. We identify and evaluate several relevant factors which can be automatically computed from this data, such as the modification of sensitive files, outlier change properties, or a lack of trust in the commit's author. Our tool, Anomalicious, automatically computes these factors and considers them holistically using a rule-based decision model. In an evaluation on a data set of 15 malware-infected repositories, Anomalicious showed promising results and identified 53.33% of malicious commits, while flagging less than 1% of commits for most repositories. Additionally, the tool found other interesting anomalies that are not related to malicious commits in an analysis of repositories with no known malicious commits.