Frequency modulated continuous wave (FMCW) millimeter wave (mmWave) radars play a key role in many advanced driver assistance systems (ADAS) on today\'s vehicles. While previous studies have only demonstrated successful false positive spoofing attacks against these sensors, all but one of them assume that the attacker knows the victim radar\'s configuration at runtime. In this work, we introduce MadRadar, a generic black-box radar attack framework for automotive mmWave FMCW radars that estimates the victim radar\'s configuration in real time and then performs attacks based on the estimation. We evaluate the impact of such attacks to maliciously manipulate the victim radar\'s point cloud and demonstrate the novel ability to effectively \"add\" (i.e., false positive attacks), \"remove\" (i.e., false positive attacks), or \"move\" (i.e., translation attacks) object detections in the victim vehicle scene. Finally, we experimentally demonstrate the feasibility of the attacks on real case studies using a live physical prototype on a software defined radio platform.
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