arXiv 论文：LLM 用于交通领域的安全与隐私研究存在明显缺口
On the security and privacy of LLMs in Mobility
35 篇交通+LLM 的论文里只有一篇做过漏洞评估，想搞车联网 AI 的工程师可以看看这篇综述
一篇 arXiv 论文综述了 LLM 在出行与交通领域的应用，覆盖的全球汽车市场规模约 2.9 万亿美元，涉及超过 15 亿辆汽车。研究团队依据欧洲 AI Act 对交通 AI 的高风险分类，拆解出九个技术类别用于评估现有研究。在 35 篇被综述工作中，超过 50% 只研究 GPT 和 Llama 模型及交通应用，仅 1 篇做了部分漏洞评估、1 篇部分风险管理系统。论文指出，安全、隐私和可靠性研究被大面积忽略，合规短板主要源于只关注静态性能而非全生命周期安全。
On the security and privacy of LLMs in Mobility
The mobility sector is undergoing a paradigm shift driven by advances in Generative Artificial Intelligence. With a global market valued at approximately 2.9 trillion dollars annually, considering only cars, the integration of these technologies has the potential to impact more than 1.5 billion vehicles worldwide. As Large Language Models (LLMs) are increasingly adopted in mobility, concerns about cybersecurity, privacy, and reliability emerge. Accordingly, this paper surveys current applications and assesses these challenges. Since the European AI Act classifies transportation AI as high risk, we derive nine technical classes from its requirements to assess current research and future deployments. Our findings show that research mainly studies GPT and Llama models (over 50\% of reviewed works) and traffic applications while largely neglecting security, privacy, and reliability. This gap extends to AI Act compliance: among 35 reviewed works, only one includes a partial vulnerability assessment and one a partial risk management system. We identify a clear gap between strong optimization performance and regulatory adherence, suggesting compliance is limited less by technology than by a focus on static performance over lifecycle safety, and underscoring an urgent need for security-by-design in safety-critical intelligent transportation systems.
