  The widespread adoption of the Android operating system has made malicious
Android applications an appealing target for attackers. Machine learning-based
(ML-based) Android malware detection (AMD) methods are crucial in addressing
this problem; however, their vulnerability to adversarial examples raises
concerns. Current attacks against ML-based AMD methods demonstrate remarkable
performance but rely on strong assumptions that may not be realistic in
real-world scenarios, e.g., the knowledge requirements about feature space,
model parameters, and training dataset. To address this limitation, we
introduce AdvDroidZero, an efficient query-based attack framework against
ML-based AMD methods that operates under the zero knowledge setting. Our
extensive evaluation shows that AdvDroidZero is effective against various
mainstream ML-based AMD methods, in particular, state-of-the-art such methods
and real-world antivirus solutions.
