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

TL;DR

This paper analyzes security vulnerabilities in scientific data analysis container images, comparing vulnerability scanners and evaluating methods like updates and minification to reduce risks.

Contribution

It provides a comprehensive vulnerability analysis of scientific container images, highlighting effective strategies for vulnerability reduction and offering practical recommendations.

Findings

Neuroscience container images contain hundreds of vulnerabilities.

Software updates remove about two thirds of vulnerabilities.

Removing unused packages effectively reduces vulnerabilities.

Abstract

Software containers greatly facilitate the deployment and reproducibility of scientific data analyses in various platforms. However, container images often contain outdated or unnecessary software packages, which increases the number of security vulnerabilities in the images, widens the attack surface in the container host, and creates substantial security risks for computing infrastructures at large. This paper 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.