Control and Data Flow in Security Smell Detection for Infrastructure as Code: Is It Worth the Effort?

Infrastructure as Code is the practice of developing and maintaining computing infrastructure through executable source code. Unfortunately, IaC has also brought about new cyber attack vectors. Prior work has therefore proposed static analyses that detect security smells in Infrastructure as Code files. However, they have so far remained at a shallow level, disregarding the control and data flow of the scripts under analysis, and may lack awareness of specific syntactic constructs. These limitations inhibit the quality of their results. To address these limitations, in this paper, we present GASEL, a novel security smell detector for the Ansible IaC language. It uses graph queries on program dependence graphs to detect 7 security smells. Our evaluation on an oracle of 243 real-world security smells and comparison against two state-of-the-art security smell detectors shows that awareness of syntax, control flow, and data flow enables our approach to substantially improve both precision and recall. We further question whether the additional effort required to develop and run such an approach is justified in practice. To this end, we investigate the prevalence of indirection through control and data flow in security smells across more than 15 000 Ansible scripts. We find that over 55% of security smells contain data-flow indirection, and over 32% require a whole-project analysis to detect. These findings motivate the need for deeper static analysis tools to detect security vulnerabilities in IaC.