# Security Risks of Agentic Vehicles: A Systematic Analysis of Cognitive and Cross-Layer Threats

arXiv: https://arxiv.org/abs/2512.17041 · HTML: https://mlsanity.com/paper/2512.17041/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 4 of 4)

- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Sentinel Agents for Secure and Trustworthy Agentic AI in Multi-Agent Systems](https://mlsanity.com/paper/2509.14956/) (2025)
- [AI Safety Assurance for Automated Vehicles: A Survey on Research, Standardization, Regulation](https://mlsanity.com/paper/2504.18328/) (2025)
- [SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks](https://mlsanity.com/paper/2512.00834/) (2025)

## Built on by (most cited first, 6 of 6)

- [MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security](https://mlsanity.com/paper/2604.07551/) (2026)
- [From Secure Agentic AI to Secure Agentic Web: Challenges, Threats, and Future Directions](https://mlsanity.com/paper/2603.01564/) (2026)
- [A Survey on Foundations and Frontiers of Multimodal Agentic Frameworks: Techniques and Applications](https://mlsanity.com/paper/2608.20379/) (2026)
- [Real-Time In-Cabin Driver Behavior Recognition on Low-Cost Edge Hardware](https://mlsanity.com/paper/2512.22298/) (2025)
- [PulseCX: Breaking the Closed-World Assumption in Real-Time CX](https://mlsanity.com/paper/2606.21124/) (2026)
- [When Agents Meet Electric Bus Fleet Operations: Pricing Behavior, Trade-offs, and Policy Implications in an Aggregator Framework](https://mlsanity.com/paper/2606.26400/) (2026)

## Abstract (arXiv)

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.