Resource-Aware Stealthy Attacks in Vehicle Platoons

TL;DR

This work analyzes stealthy, trajectory-manipulating attacks in vehicle platoons, revealing that attackers can covertly steer followers to track attacker states under various topologies and controller designs. By modeling the attack as false-data injection on V2V links and deriving Theorems 1–5, the authors link required disruption, disclosure, and topology knowledge to attack feasibility, even when leader channels are secured. The results expose critical vulnerabilities in both dynamic and static distributed controllers and motivate resource-aware resiliency measures, including privacy-preserving data sharing, topology switching, and enhanced residual-based detection. The findings offer actionable guidance for designing secure CAV platoons that maintain safety and string stability amid sophisticated, covert adversaries.

Abstract

Connected and Autonomous Vehicles (CAVs) are transforming modern transportation by enabling cooperative applications such as vehicle platooning, where multiple vehicles travel in close formation to improve efficiency and safety. However, the heavy reliance on inter-vehicle communication makes platoons highly susceptible to attacks, where even subtle manipulations can escalate into severe physical consequences. While existing research has largely focused on defending against attacks, far less attention has been given to stealthy adversaries that aim to covertly manipulate platoon behavior. This paper introduces a new perspective on the attack design problem by demonstrating how attackers can guide platoons toward their own desired trajectories while remaining undetected. We outline conditions under which such attacks are feasible, analyze their dependence on communication topologies and control protocols, and investigate the resources required by the attacker. By characterizing the resources needed to launch stealthy attacks, we address system vulnerabilities and informing the design of resilient countermeasures. Our findings reveal critical weaknesses in current platoon architectures and anomaly detection mechanisms and provide methods to develop more secure and trustworthy CAV systems.

Figures (8)

• Figure 1: Cyber-attack scenarios based on the attackers resources and objectives in the k-Nearest Neighbor Leader Tracking topology: (a) attacks on the communication channels of the leader to lead all the follower vehicles to track the attacker, (b) attacks on the followers not directly connected to the leader to lead the follower vehicles that do not have any direct communication channels from the leader to track the attacker, (c) attacks on a subset of followers not directly connected to the leader to lead this subset to track the attacker.
• Figure 2: Switching the communication channels and the topology in order to increase resiliency against attacks. (a) The system where the communication channels of the leader are subject to attacks. (b) The leader disconnects from the second follower and connects to the third follower, sending information to the third follower without being corrupted by the attacks.
• Figure 3: First scenario where the communication channels of the leader are compromised.
• Figure 4: Residuals of the followers where the communication channels of the leader are compromised.
• Figure 5: Second Scenario where the attacker launches FDI attacks on the communication channels of follower vehicles $3$ and $4$.