Abstract

Software containers greatly facilitate the deployment and reproducibility of scientific data analyses on high-performance computing clusters (HPC). However, container images often contain outdated or unnecessary software packages, which increases the number of security vulnerabilities in the images and widens the attack surface of the infrastructure. This thesis presents a vulnerability analysis of container images for scientific data analysis. We compare results obtained with four vulnerability scanners,
focusing on the use case of neuroscience data analysis, and quantifying the effect of image update and minification on the number of vulnerabilities. We find that container images used for neuroscience data analysis contain hundreds of vulnerabilities, that software updates remove about two thirds of these vulnerabilities, and that removing unused packages is also effective. We conclude with recommendations on how
to build container images with a reduced amount of vulnerabilities.