# Attack End-to-End Autonomous Driving through Module-Wise Noise

arXiv: https://arxiv.org/abs/2409.07706 · HTML: https://mlsanity.com/paper/2409.07706/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 2 of 2)

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)

## Built on by (most cited first, 7 of 7)

- [Visual Adversarial Attack on Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2411.18275/) (2024)
- [MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving](https://mlsanity.com/paper/2508.06534/) (2025)
- [SABER: Spatially Consistent 3D Universal Adversarial Objects for BEV Detectors](https://mlsanity.com/paper/2505.22499/) (2025)
- [Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective](https://mlsanity.com/paper/2607.05783/) (2026)
- [AD2: Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systems](https://mlsanity.com/paper/2602.10160/) (2026)
- [Adversarial Flow Matching for Imperceptible Attacks on End-to-End Autonomous Driving](https://mlsanity.com/paper/2605.00880/) (2026)
- [Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations](https://mlsanity.com/paper/2606.30807/) (2026)

## Abstract (arXiv)

With recent breakthroughs in deep neural networks, numerous tasks within autonomous driving have exhibited remarkable performance. However, deep learning models are susceptible to adversarial attacks, presenting significant security risks to autonomous driving systems. Presently, end-to-end architectures have emerged as the predominant solution for autonomous driving, owing to their collaborative nature across different tasks. Yet, the implications of adversarial attacks on such models remain relatively unexplored. In this paper, we conduct comprehensive adversarial security research on the modular end-to-end autonomous driving model for the first time. We thoroughly consider the potential vulnerabilities in the model inference process and design a universal attack scheme through module-wise noise injection. We conduct large-scale experiments on the full-stack autonomous driving model and demonstrate that our attack method outperforms previous attack methods. We trust that our research will offer fresh insights into ensuring the safety and reliability of autonomous driving systems.