mlsanityData to 7 Oct 2026

Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectors

arXiv 2602.09740 ↗First posted 10 Feb 2026cs.CVcited by 0

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Abstract (arXiv)

Connected and Autonomous Vehicles (CAVs), which is critical for achieving Level-5 autonomous driving. Safe and reliable CAV navigation depends on robust vision systems that enable accurate detection of objects, lane markings, and traffic signs. This survey presents a reference architecture for CAV vision systems (CAVVS) and uses it to derive a system-level threat model that maps assets, vulnerabilities, and attack points to three concrete attack surfaces (data, model, input) across the perception lifecycle. We examine attack vectors targeting each surface, rigorously evaluating their implications for confidentiality, integrity, and availability (CIA) and comparing them by adversary capability and practical exposure. We further outline atomic road events that can impact CAVVS robustness and propose a benchmark-oriented framework of datasets, metrics, and evaluation protocols for standardized future assessment. Together, these contributions provide a structured basis for vulnerability assessment, principled defense design, and reproducible robustness evaluation in real-world CAV vision systems.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 103 references that are in the map. First posted on arXiv 10 Feb 2026.

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33 of 103 references.

References · 33

  1. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  2. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 844
  3. 2021Membership Inference Attacks From First Principlescited by 839
  4. 2021Membership Inference Attacks on Machine Learning: A Surveycited by 306
  5. 2018ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detectorcited by 159
  6. 2020Physically Realizable Adversarial Examples for LiDAR Object Detectioncited by 137
  7. 2023Motion Planning for Autonomous Driving: The State of the Art and Future Perspectivescited by 112
  8. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  9. 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
  10. 2023Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Surveycited by 101
  11. 2021Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Surveycited by 81
  12. 2022I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defencescited by 75
21 more
  1. 2023Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveyscited by 75
  2. 2022Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detectioncited by 49
  3. 2020Computing Systems for Autonomous Driving: State-of-the-Art and Challengescited by 47
  4. 2022Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real Worldcited by 42
  5. 2023Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attackcited by 37
  6. 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 29
  7. 2019Open DNN Box by Power Side-Channel Attackcited by 28
  8. 2023Unified Adversarial Patch for Cross-modal Attacks in the Physical Worldcited by 27
  9. 2021Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehiclescited by 26
  10. 2019Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Modelscited by 19
  11. 2024Machine Learning Data Practices through a Data Curation Lens: An Evaluation Frameworkcited by 18
  12. 2019Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Predictioncited by 16
  13. 2023Adversarial Attacks on Traffic Sign Recognition: A Surveycited by 13
  14. 2020A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehiclescited by 10
  15. 2023Radars for Autonomous Driving: A Review of Deep Learning Methods and Challengescited by 10
  16. 2022Side-channel attack analysis on in-memory computing architecturescited by 6
  17. 2024Invisible Optical Adversarial Stripes on Traffic Sign against Autonomous Vehiclescited by 5
  18. 2024Data Poisoning Attacks in Intelligent Transportation Systems: A Surveycited by 4
  19. 2025Invisible Triggers, Visible Threats! Road-Style Adversarial Creation Attack for Visual 3D Detection in Autonomous Drivingcited by 3
  20. 2025An Investigation of Visual Foundation Models Robustnesscited by 2
  21. 2025SABER: Spatially Consistent 3D Universal Adversarial Objects for BEV Detectorscited by 1

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