# MadRadar: A Black-Box Physical Layer Attack Framework on mmWave Automotive FMCW Radars

arXiv: https://arxiv.org/abs/2311.16024 · HTML: https://mlsanity.com/paper/2311.16024/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [Probabilistic Segmentation for Robust Field of View Estimation](https://mlsanity.com/paper/2503.07375/) (2025)
- [PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting](https://mlsanity.com/paper/2505.05183/) (2025)
- [Adversarial Robustness of Camera-Radar Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2610.16012/) (2026)

## Abstract (arXiv)

Frequency modulated continuous wave (FMCW) millimeter-wave (mmWave) radars play a critical role in many of the advanced driver assistance systems (ADAS) featured on today's vehicles. While previous works have demonstrated (only) successful false-positive spoofing attacks against these sensors, all but one assumed that an attacker had the runtime knowledge of the victim radar's configuration. In this work, we introduce MadRadar, a general black-box radar attack framework for automotive mmWave FMCW radars capable of estimating the victim radar's configuration in real-time, and then executing an attack based on the estimates. We evaluate the impact of such attacks maliciously manipulating a victim radar's point cloud, and show the novel ability to effectively `add' (i.e., false positive attacks), `remove' (i.e., false negative attacks), or `move' (i.e., translation attacks) object detections from a victim vehicle's scene. Finally, we experimentally demonstrate the feasibility of our attacks on real-world case studies performed using a real-time physical prototype on a software-defined radio platform.