# Beyond Crash: Hijacking Your Autonomous Vehicle for Fun and Profit

arXiv: https://arxiv.org/abs/2602.07249 · HTML: https://mlsanity.com/paper/2602.07249/

## Key facts

- Cited by 1 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 23 of its 56 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## 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, 23 of 23)

- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Bench2Drive: Towards Multi-Ability Benchmarking of Closed-Loop End-To-End Autonomous Driving](https://mlsanity.com/paper/2406.03877/) (2024)
- [Enhanced Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/2111.09679/) (2021)
- [Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline](https://mlsanity.com/paper/2206.08129/) (2022)
- [Blind Backdoors in Deep Learning Models](https://mlsanity.com/paper/2005.03823/) (2020)
- [SimLingo: Vision-Only Closed-Loop Autonomous Driving with Language-Action Alignment](https://mlsanity.com/paper/2503.09594/) (2025)
- [Graph Backdoor](https://mlsanity.com/paper/2006.11890/) (2020)
- [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)
- [Local Gradients Smoothing: Defense against localized adversarial attacks](https://mlsanity.com/paper/1807.01216/) (2018)
- [Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection](https://mlsanity.com/paper/2112.04532/) (2021)
- [Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack](https://mlsanity.com/paper/2009.06701/) (2020)
- [An LLM-Assisted Easy-to-Trigger Backdoor Attack on Code Completion Models: Injecting Disguised Vulnerabilities against Strong Detection](https://mlsanity.com/paper/2406.06822/) (2024)
- [You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks](https://mlsanity.com/paper/2210.09482/) (2022)
- [ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms](https://mlsanity.com/paper/2302.11408/) (2023)
- [Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems](https://mlsanity.com/paper/2201.06192/) (2022)
- [Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles](https://mlsanity.com/paper/2106.07098/) (2021)
- [Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data](https://mlsanity.com/paper/2302.06279/) (2023)
- [Rolling Colors: Adversarial Laser Exploits against Traffic Light Recognition](https://mlsanity.com/paper/2204.02675/) (2022)
- [Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World Attacks](https://mlsanity.com/paper/2201.04610/) (2022)
- [DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems](https://mlsanity.com/paper/2110.03154/) (2021)
- [DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data](https://mlsanity.com/paper/2503.21305/) (2025)
- [CANflict: Exploiting Peripheral Conflicts for Data-Link Layer Attacks on Automotive Networks](https://mlsanity.com/paper/2209.09557/) (2022)
- [Kidnapping Deep Learning-based Multirotors using Optimized Flying Adversarial Patches](https://mlsanity.com/paper/2308.00344/) (2023)

## Built on by (most cited first, 1 of 1)

- [Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses](https://mlsanity.com/paper/2605.02900/) (2026)

## Abstract (arXiv)

Autonomous Vehicles (AVs), especially vision-based AVs, are rapidly being deployed without human operators. As AVs operate in safety-critical environments, understanding their robustness in an adversarial environment is an important research problem. Prior physical adversarial attacks on vision-based autonomous vehicles predominantly target immediate safety failures (e.g., a crash, a traffic-rule violation, or a transient lane departure) by inducing a short-lived perception or control error. This paper shows a qualitatively different risk: a long-horizon route integrity compromise, where an attacker gradually steers a victim AV away from its intended route and into an attacker-chosen destination while the victim continues to drive ``normally.'' This will not pose a danger to the victim vehicle itself, but also to potential passengers sitting inside the vehicle, who may not notice the route changes. In this paper, we design and implement the first adversarial framework, called JackZebra, which performs route-level hijacking of a vision-based end-to-end driving stack using a physically plausible attacker vehicle with a reconfigurable display and a camera sensor mounted on the rear. The central challenge is temporal persistence: adversarial influence must remain effective in changing viewpoints, lighting, weather, traffic, and the victim's continual replanning -- without triggering conspicuous failures. Our key insight is to treat route hijacking as a closed-loop control problem and to convert adversarial patches into steering primitives that can be selected online via an interactive adjustment loop based on observed victim behavior using the rear camera. Our evaluations in both simulated and real-world scenarios show that JackZebra can successfully hijack victim vehicles to deviate from original routes and stop at places designated by the adversary with a high success rate.