Detecting Backdoors in Collaboration Graphs of Software Repositories
Tom Ganz, Inaam Ashraf, Martin Härterich, Konrad Rieck · 2023
Software backdoors pose a major threat to the security of computer systems. Minor modifications to a program are often sufficient to undermine security mechanisms and enable unauthorized access to a system. The direct approach of detecting backdoors using static or dynamic program analysis is a daunting task that becomes increasingly futile with the attacker's capabilities. As a remedy, we introduce an orthogonal strategy for the detection of software backdoors. Instead of searching for concealed functionality in program code, we propose to analyze how a software has been developed and locate clues for malicious activities in its version history, such as in a Git repository. To this end, we model the version history as a collaboration graph that reflects how, when and where developers have committed changes to the software. We develop a method for anomaly detection using graph neural networks that builds on this representation and is able to detect spatial and temporal anomalies in the development process. % We evaluate our approach using a collection of real-world backdoors added to Github repositories. Compared to previous work, our method identifies a significantly larger number of backdoors with a low false-positive rate. While our approach cannot rule out the presence of software backdoors, it provides an alternative detection strategy that complements existing work focused only on program analysis.