2025/12/18 by Ali Eslami, Jiangbo Yu, Eslami, Ali +1 · 2 citations
Engineering · Psychology · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Cognition #Control (management) #FOS: Computer and information sciences #FOS: Electrical engineering #Foundation (evidence) #Human-Automation Interaction and Safety #Perception #Risk assessment #Risk perception #Security controls #Systems and Control (eess.SY) #Vehicular Ad Hoc Networks (VANETs) #electronic engineering #information engineering
paper · open access · doi:10.48550/arxiv.2512.17041
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/12/18 · openalex created_date 2025/12/23 · openalex updated_date 2026/07/28
Agentic AI is increasingly being explored and introduced in both manually driven and autonomous vehicles, leading to the notion of Agentic Vehicles (AgVs), with capabilities such as memory-based personalization, goal interpretation, strategic reasoning, and tool-mediated assistance. While frameworks such as the OWASP Agentic AI Security Risks highlight vulnerabilities in reasoning-driven AI systems, they are not designed for safety-critical cyber-physical platforms such as vehicles, nor do they account for interactions with other layers such as perception, communication, and control layers. This paper investigates security threats in AgVs, including OWASP-style risks and cyber-attacks from other layers affecting the agentic layer. By introducing a role-based architecture for agentic vehicles, consisting of a Personal Agent and a Driving Strategy Agent, we will investigate vulnerabilities in both agentic AI layer and cross-layer risks, including risks originating from upstream layers (e.g., perception layer, control layer, etc.). A severity matrix and attack-chain analysis illustrate how small distortions can escalate into misaligned or unsafe behavior in both human-driven and autonomous vehicles. The resulting framework provides the first structured foundation for analyzing security risks of agentic AI in both current and emerging vehicle platforms.