Intelligent Transportation Systems

This paper provides one of the first comprehensive security evaluations of Large Vision-Language Models in Intelligent Transportation Systems. We introduce a transportation-specific jailbreak benchmark, propose a novel attack combining image typography manipulation with multi-turn prompting, and develop a multi-layered defense integrating rule-based filtering and zero-shot classification. Extensive experiments on both open-source and proprietary LVLMs demonstrate the effectiveness of the proposed methods and highlight critical security challenges for deploying multimodal AI in safety-critical transportation systems.