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Numerous open-source and commercial malware detectors are available. However,their efficacy is threatened by new adversarial attacks, whereby malwareattempts to evade detection, e.g., by performing feature-space manipulation. Inthis work, we propose an explainability-guided and model-agnostic testingframework for robustness of malware detectors when confronted with adversarialattacks. The framework introduces the concept of Accrued Malicious Magnitude(AMM) to identify which malware features could be manipulated to maximize thelikelihood of evading detection. We then use this framework to test severalstate-of-the-art malware detectors' abilities to detect manipulated malware. Wefind that (i) commercial antivirus engines are vulnerable to AMM-guided testcases; (ii) the ability of a manipulated malware generated using one detectorto evade detection by another detector (i.e., transferability) depends on theoverlap of features with large AMM values between the different detectors; and(iii) AMM values effectively measure the fragility of features (i.e.,capability of feature-space manipulation to flip the prediction results) andexplain the robustness of malware detectors facing evasion attacks. Ourfindings shed light on the limitations of current malware detectors, as well ashow they can be improved.

Mate! Are You Really Aware? An Explainability-Guided Testing Framework for Robustness of Malware Detectors