March 5, 2024

Engineers Develop Revolutionary Hack to Manipulate Automotive Radar Systems

In a groundbreaking development, engineers from Duke University have devised a system called MadRadar that has the ability to manipulate automotive radar sensors, creating false images and tricking the system into believing in nonexistent objects or altered trajectories. Led by Miroslav Pajic, the Dickinson Family Associate Professor of Electrical and Computer Engineering, and Tingjun Chen, Assistant Professor of Electrical and Computer Engineering, the researchers have demonstrated the capabilities of MadRadar, which pose a significant threat to radar security.

The technology can effectively hide the presence of an actual vehicle, create phantom cars, and even deceive the radar into thinking that a real vehicle has deviated from its path. What makes MadRadar particularly formidable is that it can execute these manipulations in real-time and without prior knowledge of the target radar’s specific settings. This makes it the most troublesome threat to radar security to date.

The team of engineers at Duke University urges car manufacturers to take immediate action to enhance the security of their radar systems in response to the vulnerabilities exposed by MadRadar. The research, currently available on the arXiv preprint server, will be published in the 2024 Network and Distributed System Security Symposium, a prestigious event taking place from February 26 to March 1 in San Diego, California.

Explaining their findings, Professor Pajic says, “Without knowing much about the targeted car’s radar system, we can make a fake vehicle appear out of nowhere or make an actual vehicle disappear in real-world experiments. We’re not building these systems to hurt anyone; we’re demonstrating the existing problems with current radar systems to show that we need to fundamentally change how we design them.”

Radar technology is widely used in modern cars equipped with assistive and autonomous driving systems for detecting moving vehicles in front of and around the car. It complements the visual and laser-based systems in detecting vehicles ahead or behind the vehicle. However, due to the wide variety of cars on the road, each with its own unique radar operating parameters, it is difficult to exploit the same vulnerabilities across different vehicles.

Traditionally, radar-spoofing systems required precise knowledge of the targeted radar’s specific parameters, such as operating frequencies and intervals. However, MadRadar offers a solution to this challenge. In their demonstration, the Duke University team successfully developed a radar-spoofing system that can accurately detect a car’s radar parameters in less than a quarter of a second. Armed with this information, the system can then transmit its own radar signals to deceive the target radar.

To further illustrate the potential risks, the researchers provided an example scenario: a driver cruising in an autonomous vehicle suddenly receives flashing lights and audible warnings indicating an impending collision. The driver looks at the dashboard screen, which displays an approaching car on a collision course. Except, when they look through the windshield, they see nothing. Confused, the driver allows the autopilot to take control, which swerves the car into a ditch. It turns out that the radar had been fooled into detecting a non-existent threat, while the actual danger was a group of hijackers approaching the immobilized vehicle.

The development of MadRadar underscores the urgent need for enhanced radar security measures in modern cars. As autonomous driving technology advances, the reliability and security of radar systems become paramount. Car manufacturers need to rethink and revamp the design of radar systems to ensure they are resistant to potential spoofing attacks. The MadRadar system serves as a wake-up call, urging the industry to prioritize cybersecurity and develop robust countermeasures against such threats.

1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it