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论出行领域中 LLM 的安全与隐私
该 arXiv 综述考察 LLM 在出行领域的应用与安全隐私挑战。作者依据欧盟 AI Act 对交通 AI 的高风险分类，推导出九个技术类别来评估现有研究与未来部署。结果显示，超过 50% 的被综述工作集中于 GPT 和 Llama 模型及交通应用，而安全、隐私和可靠性研究明显不足；在 35 篇被综述工作中，仅一篇包含部分漏洞评估，一篇包含部分风险管理系统。作者指出优化性能与合规之间存在明显差距，呼吁在安全攸关的智能交通系统中采用安全设计。
AI推荐理由 arXiv 预印本综述，作者为 Mauro Conti 等，聚焦出行领域 LLM 的安全与隐私。具体新信息：从欧盟 AI Act 高风险要求推导出九个技术类别，用于评估现有研究；统计显示超过 50% 被综述工作集中在 GPT 和 Llama 模型及交通应用，而安全、隐私与可靠性被明显忽视；在 35 篇被综述工作中，仅一篇包含部分漏洞评估、一篇包含部分风险管理系统。局限：这是综述而非新实验，结论基于对既有文献的统计，且为预印本未经同行评审，摘要未给出具体漏洞类型或缓解方案。
72
正文
Computer Science > Cryptography and Security
arXiv:2609.26295 (cs)
[Submitted on 22 Sep 2026]
Title:On the security and privacy of LLMs in Mobility
Authors:Mauro Conti, Lorenzo Perinello, Umberto Salviati
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Abstract: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.
Subjects:
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
Cite as:
arXiv:2609.26295 [cs.CR]
(or
arXiv:2609.26295v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2609.26295
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Umberto Salviati [view email]
[v1]
Tue, 22 Sep 2026 12:09:08 UTC (133 KB)
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