secml-malware: A Python Library for Adversarial Robustness Evaluation of Windows Malware Classifiers

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.