  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. \secmlmalware 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.
