Analyzing the Impact of Adversarial Attacks on C-V2X-Enabled Road Safety: An Age of Information Perspective

2026-03-03

AI Summary

This study addresses a critical security vulnerability in NR-V2X resource allocation mechanisms, which are susceptible to a novel resource starvation attack that degrades the service-level requirements (SLR) of safety-critical autonomous driving applications, thereby endangering road safety. To quantify the impact of such adversarial attacks on C-V2X safety performance, the paper introduces Age of Information (AoI) as a novel metric and develops a discrete-time Markov chain (DTMC) model for analytical and simulation-based evaluation. Results demonstrate that the proposed attack can deteriorate SLR by up to 15%. Furthermore, the analysis reveals that in resource-constrained scenarios, selfish behavior by benign vehicles—such as aggressive resource reservation—can inadvertently exacerbate the security risks, amplifying the overall system vulnerability.

Technology Category

• Application Domains: Mobility, Driving & Flight
• Computer Vision: Adversarial Attacks & Robustness
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

The Cellular Vehicle-to-Everything (C-V2X), introduced and developed by the 3GPP, is a promising technology for the Autonomous Driving System (ADS). C-V2X aims to fulfill the Service-Level Requirements (SLRs) of ADS to ensure road safety following the development of the latest version, i.e., the NR-V2X. However, vulnerabilities threatening road safety in NR-V2X persist that have yet to be investigated. Existing research primarily evaluates road safety based on successful packet receptions. In this work, we propose a novel resource starvation attack that exploits vulnerabilities in the resource allocation of NR-V2X to diminish the required SLRs, making the road condition unsafe for autonomous driving. Furthermore, we establish the Age of Information (AoI) as the predominant metric for estimating the impact of adversarial attacks on NR-V2X by constructing a Discrete-time Markov chain (DTMC) based analytical model and validating it through extensive simulations. Finally, our analysis underscores how the proposed attack on NR-V2X can lead to unsafe driving conditions by reducing the SLR of time-sensitive applications in ADS up to 15% from the target. Additionally, we observe that even benign vehicles act selfishly when resources are scarce, leading to further safety compromises.

Problem

• Adversarial Attacks
• C-V2X
• Age of Information
• Resource Starvation
• Road Safety

Innovation

• resource starvation attack
• Age of Information
• NR-V2X
• Discrete-time Markov chain
• C-V2X security