# MLSMM: Machine Learning Security Maturity Model

arXiv: https://arxiv.org/abs/2306.16127 · HTML: https://mlsanity.com/paper/2306.16127/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Adversarial Machine Learning -- Industry Perspectives](https://mlsanity.com/paper/2002.05646/) (2020)
- [Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning](https://mlsanity.com/paper/2205.01992/) (2022)
- [Modeling Realistic Adversarial Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2106.09380/) (2021)
- [Machine Learning Security in Industry: A Quantitative Survey](https://mlsanity.com/paper/2207.05164/) (2022)
- [Threat Assessment in Machine Learning based Systems](https://mlsanity.com/paper/2207.00091/) (2022)

## Abstract (arXiv)

Assessing the maturity of security practices during the development of Machine Learning (ML) based software components has not gotten as much attention as traditional software development. In this Blue Sky idea paper, we propose an initial Machine Learning Security Maturity Model (MLSMM) which organizes security practices along the ML-development lifecycle and, for each, establishes three levels of maturity. We envision MLSMM as a step towards closer collaboration between industry and academia.