{"ID":2893840,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.12003","arxiv_id":"2507.12003","title":"Expanding ML-Documentation Standards For Better Security","abstract":"This article presents the current state of ML-security and of the documentation of ML-based systems, models and datasets in research and practice based on an extensive review of the existing literature. It shows a generally low awareness of security aspects among ML-practitioners and organizations and an often unstandardized approach to documentation, leading to overall low quality of ML-documentation. Existing standards are not regularly adopted in practice and IT-security aspects are often not included in documentation. Due to these factors, there is a clear need for improved security documentation in ML, as one step towards addressing the existing gaps in ML-security. To achieve this, we propose expanding existing documentation standards for ML-documentation to include a security section with specific security relevant information. Implementing this, a novel expanded method of documenting security requirements in ML-documentation is presented, based on the existing Model Cards and Datasheets for Datasets standards, but with the recommendation to adopt these findings in all ML-documentation.","short_abstract":"This article presents the current state of ML-security and of the documentation of ML-based systems, models and datasets in research and practice based on an extensive review of the existing literature. It shows a generally low awareness of security aspects among ML-practitioners and organizations and an often unstanda...","url_abs":"https://arxiv.org/abs/2507.12003","url_pdf":"https://arxiv.org/pdf/2507.12003v1","authors":"[\"Cara Ellen Appel\"]","published":"2025-07-16T07:57:57Z","proceeding":"cs.CR","tasks":"[\"cs.CR\",\"cs.LG\",\"cs.SE\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}