Attackers target vulnerabilities within the vehicle's WiFi communication stack, gaining unauthorized access or control.
An attacker can unlock a car and control other critical functions through malicious SMS.
RDS Attacks involves tricking victims into installing a benign-looking app that uses the RDS interface. Initially, this app exhibits no malicious behavior. Post-installation, the app dynamically downloads a backdoor, reassembling RDS packets to execute the payload. The exploit remains undetected as antiviruses can't scan runtime downloads. The attack bypasses Android's security checks of the vehicles infotainment system, exploiting vulnerabilities in the FM Radio API.
This technique focuses on compromising or manipulating RSU}/Balises and thus the communication and data exchange between vehicles and the infrastructure elements. Such attacks can disrupt the normal functioning of a vehicle by feeding it misleading information or by blocking essential signals, potentially leading to unsafe conditions or operational inefficiencies.
The TPMS communications are based on standard modulation schemes and simple protocols without cryptographic mechanisms. The in-vehicle system appears to fully trust all received messages, lacking basic security practices like input validation. This makes spoofing attacks feasible, allowing adversaries to send counterfeit messages, causing the TPMS to malfunction. Transmissions can be spoofed from the roadside or a nearby vehicle.
The significant communication range of TPMS messages is approx 10 meters from the ITS with a basic antenna, and up to 40 meters with an low noise amplifier. Attackers can interfere with or jam these TPMS message transmissions from a distance.
Black Hole Attacks in VANET involve malicious vehicles that drop all received packets instead of forwarding them. The objectives of these attacks are to inhibit the forwarding of packets from one vehicle to its neighboring "destination node", prevent the reception of packets from other vehicles, and disrupt the overall communication network, thereby posing significant threats to the safety and functionality of self-driving ITS.
FMCW radars emit electromagnetic waves and measure reflections to determine object distances and velocities. An attacker vehicle is positioned in front of the victims vehicle. The attacker uses a modified radar system, transmitting a powerful signal that overpowers the real reflected signal. The attacker's radar measures the true relative distance and velocity from the victim's vehicle to execute the attack.
By illuminating the LiDAR with a strong light of the same wavelength , attackers can effectively "erase" existing objects in the sensed output of the LiDAR. This means that the LiDAR would not be able to detect certain objects, potentially leading to hazardous situations.