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On the security and privacy of LLMs in Mobility

arXiv 2609.26295 ↗First posted 22 Sep 2026cs.CR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 75 references that are in the map. First posted on arXiv 22 Sep 2026.

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18 of 75 references.

References · 18

  1. 2024Jailbreak Attacks and Defenses Against Large Language Models: A Surveycited by 215
  2. 2024Attacks, Defenses and Evaluations for LLM Conversation Safety: A Surveycited by 124
  3. 2024UrbanGPT: Spatio-Temporal Large Language Modelscited by 81
  4. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  5. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  6. 2024Large Language Models are Zero-Shot Next Location Predictorscited by 22
  7. 2024Geolocation Representation from Large Language Models are Generic Enhancers for Spatio-Temporal Learningcited by 15
  8. 2024Urban Mobility Assessment Using LLMscited by 13
  9. 2025Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenizationcited by 8
  10. 2025The Hidden Threat in Plain Text: Attacking RAG Data Loaderscited by 8
  11. 2024Enhancing Traffic Incident Management with Large Language Models: A Hybrid Machine Learning Approach for Severity Classificationcited by 7
  12. 2024Harnessing LLMs for Cross-City OD Flow Predictioncited by 7
6 more
  1. 2024Leveraging Large Language Models (LLMs) for Traffic Management at Urban Intersections: The Case of Mixed Traffic Scenarioscited by 6
  2. 2025Misleading Large Language Models used (or misused) in Scientific Peer-Reviewing via Hidden Prompt-Injection Attackscited by 6
  3. 2025Urban-MAS: Human-Centered Urban Prediction with LLM-Based Multi-Agent Systemcited by 4
  4. 2025Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Modelscited by 3
  5. 2024Generative AI-enabled Digital Twins for 6G-enhanced Smart Citiescited by 1
  6. 2025EV-STLLM: Electric vehicle charging forecasting based on spatio-temporal large language models with multi-frequency and multi-scale information fusioncited by 1

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