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

arXiv: https://arxiv.org/abs/2602.09740 · HTML: https://mlsanity.com/paper/2602.09740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 103 references that are in the map.
- First posted on arXiv 10 Feb 2026.

## Builds on (most cited first, 30 of 33)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector](https://mlsanity.com/paper/1804.05810/) (2018)
- [Physically Realizable Adversarial Examples for LiDAR Object Detection](https://mlsanity.com/paper/2004.00543/) (2020)
- [Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives](https://mlsanity.com/paper/2303.09824/) (2023)
- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021)
- [Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon](https://mlsanity.com/paper/2203.03818/) (2022)
- [Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey](https://mlsanity.com/paper/2112.08936/) (2021)
- [I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences](https://mlsanity.com/paper/2206.08451/) (2022)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection](https://mlsanity.com/paper/2205.14951/) (2022)
- [Computing Systems for Autonomous Driving: State-of-the-Art and Challenges](https://mlsanity.com/paper/2009.14349/) (2020)
- [Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World](https://mlsanity.com/paper/2205.05909/) (2022)
- [Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack](https://mlsanity.com/paper/2308.11894/) (2023)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [Open DNN Box by Power Side-Channel Attack](https://mlsanity.com/paper/1907.10406/) (2019)
- [Unified Adversarial Patch for Cross-modal Attacks in the Physical World](https://mlsanity.com/paper/2307.07859/) (2023)
- [Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles](https://mlsanity.com/paper/2106.07098/) (2021)
- [Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models](https://mlsanity.com/paper/1910.06838/) (2019)
- [Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework](https://mlsanity.com/paper/2405.02703/) (2024)
- [Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction](https://mlsanity.com/paper/1904.07370/) (2019)
- [Adversarial Attacks on Traffic Sign Recognition: A Survey](https://mlsanity.com/paper/2307.08278/) (2023)
- [A Survey on Security Attacks and Defense Techniques for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2007.08041/) (2020)
- [Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges](https://mlsanity.com/paper/2306.09304/) (2023)
- [Side-channel attack analysis on in-memory computing architectures](https://mlsanity.com/paper/2209.02792/) (2022)
- [Invisible Optical Adversarial Stripes on Traffic Sign against Autonomous Vehicles](https://mlsanity.com/paper/2407.07510/) (2024)
- [Data Poisoning Attacks in Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2407.15855/) (2024)

## 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.