# secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python

arXiv: https://arxiv.org/abs/2104.12848 · HTML: https://mlsanity.com/paper/2104.12848/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2021.

## Built on by (most cited first, 5 of 5)

- [RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion](https://mlsanity.com/paper/2302.01757/) (2023)
- [A Comparison of Adversarial Learning Techniques for Malware Detection](https://mlsanity.com/paper/2308.09958/) (2023)
- [A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection](https://mlsanity.com/paper/2405.14519/) (2024)
- [A Comparison of State-of-the-Art Techniques for Generating Adversarial Malware Binaries](https://mlsanity.com/paper/2111.11487/) (2021)
- [Latent Stability Analysis of Malware Representations Under Feature-Space Perturbations](https://mlsanity.com/paper/2607.24896/) (2026)

## Abstract (arXiv)

Machine learning has been increasingly used as a first line of defense for Windows malware detection. Recent work has however shown that learning-based malware detectors can be evaded by carefully-perturbed input malware samples, referred to as adversarial EXEmples, thus demanding for tools that can ease and automate the adversarial robustness evaluation of such detectors. To this end, we present secml-malware, the first Python library for computing adversarial attacks on Windows malware detectors. secml-malware implements state-of-the-art white-box and black-box attacks on Windows malware classifiers, by leveraging a set of feasible manipulations that can be applied to Windows programs while preserving their functionality. The library can be used to perform the penetration testing and assessment of the adversarial robustness of Windows malware detectors, and it can be easily extended to include novel attack strategies. Our library is available at https://github.com/pralab/secml_malware.