Security smells in infrastructure as code: a taxonomy update beyond the seven sins
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908555122049024 |
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| author | War, Aicha Nikiema, Serge L. B. Samhi, Jordan Klein, Jacques Bissyande, Tegawende F. |
| author_facet | War, Aicha Nikiema, Serge L. B. Samhi, Jordan Klein, Jacques Bissyande, Tegawende F. |
| contents | Infrastructure as Code (IaC) has become essential for modern software management, yet security flaws in IaC scripts can have severe consequences, as exemplified by the recurring exploits of Cloud Web Services. Prior work has recognized the need to build a precise taxonomy of security smells in IaC scripts as a first step towards developing approaches to improve IaC security. This first effort led to the unveiling of seven sins, limited by the focus on a single IaC tool as well as by the extensive, and potentially biased, manual effort that was required. We propose, in our work, to revisit this taxonomy: first, we extend the study of IaC security smells to a more diverse dataset with scripts associated with seven popular IaC tools, including Terraform, Ansible, Chef, Puppet, Pulumi, Saltstack, and Vagrant; second, we bring in some automation for the analysis by relying on an LLM. While we leverage LLMs for initial pattern processing, all taxonomic decisions underwent systematic human validation and reconciliation with established security standards. Our study yields a comprehensive taxonomy of 62 security smell categories, significantly expanding beyond the previously known seven. We demonstrate actionability by implementing new security checking rules within linters for seven popular IaC tools, often achieving 1.00 precision score. Our evolution study of security smells in GitHub projects reveals that these issues persist for extended periods, likely due to inadequate detection and mitigation tools. This work provides IaC practitioners with insights for addressing common security smells and systematically adopting DevSecOps practices to build safer infrastructure code. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18761 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Security smells in infrastructure as code: a taxonomy update beyond the seven sins War, Aicha Nikiema, Serge L. B. Samhi, Jordan Klein, Jacques Bissyande, Tegawende F. Cryptography and Security Artificial Intelligence Machine Learning Software Engineering Infrastructure as Code (IaC) has become essential for modern software management, yet security flaws in IaC scripts can have severe consequences, as exemplified by the recurring exploits of Cloud Web Services. Prior work has recognized the need to build a precise taxonomy of security smells in IaC scripts as a first step towards developing approaches to improve IaC security. This first effort led to the unveiling of seven sins, limited by the focus on a single IaC tool as well as by the extensive, and potentially biased, manual effort that was required. We propose, in our work, to revisit this taxonomy: first, we extend the study of IaC security smells to a more diverse dataset with scripts associated with seven popular IaC tools, including Terraform, Ansible, Chef, Puppet, Pulumi, Saltstack, and Vagrant; second, we bring in some automation for the analysis by relying on an LLM. While we leverage LLMs for initial pattern processing, all taxonomic decisions underwent systematic human validation and reconciliation with established security standards. Our study yields a comprehensive taxonomy of 62 security smell categories, significantly expanding beyond the previously known seven. We demonstrate actionability by implementing new security checking rules within linters for seven popular IaC tools, often achieving 1.00 precision score. Our evolution study of security smells in GitHub projects reveals that these issues persist for extended periods, likely due to inadequate detection and mitigation tools. This work provides IaC practitioners with insights for addressing common security smells and systematically adopting DevSecOps practices to build safer infrastructure code. |
| title | Security smells in infrastructure as code: a taxonomy update beyond the seven sins |
| topic | Cryptography and Security Artificial Intelligence Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2509.18761 |