  As open-source AI software projects become an integral component in the AI
software development, it is critical to develop a novel methods to ensure and
measure the security of the open-source projects for developers. Code
ownership, pivotal in the evolution of such projects, offers insights into
developer engagement and potential vulnerabilities. In this paper, we leverage
the code ownership metrics to empirically investigate the correlation with the
latent vulnerabilities across five prominent open-source AI software projects.
The findings from the large-scale empirical study suggest a positive
relationship between high-level ownership (characterised by a limited number of
minor contributors) and a decrease in vulnerabilities. Furthermore, we
innovatively introduce the time metrics, anchored on the project's duration,
individual source code file timelines, and the count of impacted releases.
These metrics adeptly categorise distinct phases of open-source AI software
projects and their respective vulnerability intensities. With these novel code
ownership metrics, we have implemented a Python-based command-line application
to aid project curators and quality assurance professionals in evaluating and
benchmarking their on-site projects. We anticipate this work will embark a
continuous research development for securing and measuring open-source AI
project security.
