A Methodology for Automatic Test Case Generation in Intelligent Connected Vehicle Cybersecurity Testing Based on Large Language Models

Abstract

With the increasing complexity of the "cloud–pipe–end" architecture in intelligent connected vehicles (ICVs), the coexistence of multiple communication protocols such as automotive Ethernet, CAN bus, and V2X communication, coupled with the successive release of regulations and standards including UN R155, ISO/SAE 21434, and GB44495, the traditional manual approach to writing cybersecurity test cases faces severe challenges in terms of efficiency, coverage, and compliance. Large language models (LLMs), with their powerful semantic understanding and content generation capabilities, offer a new technical pathway for the automated generation of test cases. However, general-purpose LLMs lack domain-specific knowledge of automotive cybersecurity, and their direct application encounters challenges such as knowledge deficiency, insufficient scenario adaptation, and untrustworthy generated content. This paper proposes a methodological framework for automatic test case generation in ICV cybersecurity testing, named AutoCarSec. Centered on domain knowledge injection, the framework adopts a three-layer progressive architecture consisting of a knowledge enhancement layer, a scenario modeling and generation layer, and a multi-agent collaborative execution layer, systematically addressing the three core issues of knowledge acquisition, scenario modeling, and quality assurance for LLMs in the automotive cybersecurity testing domain. This paper elaborates on the detailed design of four key stages: structured construction of automotive cybersecurity domain knowledge, automatic test scenario modeling based on attack trees and TARA, LLM-driven hierarchical test case generation, and multi-agent collaborative verification with iterative optimization. It provides a logically sound and highly operable methodological reference for the intelligent transformation of cybersecurity testing for intelligent connected vehicles.