  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 well-crafted, adversarial manipulations of
input malware, highlighting the need for tools that can ease and automate the
adversarial robustness evaluation of such detectors. To this end, we
presentsecml-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 functionality-preserving manipulations that can be applied to Windows
programs without corrupting their functionality. The library can be used to
assess the adversarial robustness of Windows malware detectors, and it can be
easily extended to include novel attack strategies. It is available at
https://github.com/zangobot/secml_malware.
