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Software containers greatly facilitate the deployment and reproducibility ofscientific data analyses in various platforms. However, container images oftencontain outdated or unnecessary software packages, which increases the numberof security vulnerabilities in the images, widens the attack surface in thecontainer host, and creates substantial security risks for computinginfrastructures at large. This paper presents a vulnerability analysis ofcontainer images for scientific data analysis. We compare results obtained withfour vulnerability scanners, focusing on the use case of neuroscience dataanalysis, and quantifying the effect of image update and minification on thenumber of vulnerabilities. We find that container images used for neurosciencedata analysis contain hundreds of vulnerabilities, that software updates removeabout two thirds of these vulnerabilities, and that removing unused packages isalso effective. We conclude with recommendations on how to build containerimages with a reduced amount of vulnerabilities.

An Analysis of Security Vulnerabilities in Container Images for Scientific Data Analysis