Mitigating cyberattacks on autonomous vehicles: a comprehensive review of Generative Artificial Intelligence defense techniques
TL;DR
An AV-centric, three-dimensional taxonomy is introduced that classifies defenses along generative architecture, defensive function, and AV-relevant attack surface, explicitly anchoring each study to AV subsystems and operational contexts, and provides a disciplined synthesis that separates study-specific performance findings from broader design insights.
Abstract
Autonomous vehicles (AVs) are rapidly becoming foundational components of intelligent transportation systems (ITS), yet their complex cyber-physical architectures expose them to a broad and continuously evolving threat landscape. Existing cybersecurity solutions struggle to keep pace with the dynamic, data-intensive nature of AV ecosystems, leaving critical vulnerabilities unaddressed across perception, communication, and decision-making subsystems. Generative Artificial Intelligence (GAI), encompassing Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), has emerged as a powerful paradigm for both offensive simulation and defensive reinforcement, enabling synthetic data generation, adversarial attack emulation, and enhanced anomaly and intrusion detection. Yet despite growing interest in GAI for general cybersecurity, its systematic application to AV-specific security remains fragmented and underexplored. This paper addresses that gap through a PRISMA-guided systematic review of GAI-driven defense mechanisms for AV cybersecurity, synthesizing 216 peer-reviewed studies drawn from major scientific databases and published between January 2020 and February 2026. Three principal contributions are made. First, we introduce an AV-centric, three-dimensional taxonomy that classifies defenses along generative architecture, defensive function, and AV-relevant attack surface, explicitly anchoring each study to AV subsystems and operational contexts. Second, we provide a disciplined synthesis that separates study-specific performance findings from broader design insights, exposing fundamental gaps between conventional and GAI-based approaches in scalability, adaptability, and resilience. Third, we identify critical open challenges—including training instability, the absence of standardized AV security benchmarks, real-time deployment constraints, and limited explainability—and propose targeted research directions for safety-critical environments. By grounding GAI defenses within AV system layers and cyber-physical threat models, this review serves as a practitioner- and researcher-oriented reference for building robust, scalable, and trustworthy cybersecurity solutions for next-generation autonomous vehicles.