Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving

• URL: http://arxiv.org/abs/2507.09095v2
• Date: Wed, 01 Oct 2025 13:29:16 GMT
• Title: Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving
• Authors: Md Hasan Shahriar, Md Mohaimin Al Barat, Harshavardhan Sundar, Ning Zhang, Naren Ramakrishnan, Y. Thomas Hou, Wenjing Lou,
• Abstract summary: We introduce DejaVu, an attack that exploits the in-vehicular network and induces delays across sensor streams to create subtle temporal misalignments.<n>Our comprehensive attack analysis reveals the sensors' task-specific imbalanced sensitivities.<n>With a single-frame LiDAR delay, an attacker can reduce the car detection mAP by up to 88.5%, while with a three-frame camera delay, multiple object tracking accuracy (MOTA) for car drops by 73%.
• Score: 23.83773255227831
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal fusion (MMF) plays a critical role in the perception of autonomous driving, which primarily fuses camera and LiDAR streams for a comprehensive and efficient scene understanding. However, its strict reliance on precise temporal synchronization exposes it to new vulnerabilities. In this paper, we introduce DejaVu, an attack that exploits the in-vehicular network and induces delays across sensor streams to create subtle temporal misalignments, severely degrading downstream MMF-based perception tasks. Our comprehensive attack analysis across different models and datasets reveals the sensors' task-specific imbalanced sensitivities: object detection is overly dependent on LiDAR inputs, while object tracking is highly reliant on the camera inputs. Consequently, with a single-frame LiDAR delay, an attacker can reduce the car detection mAP by up to 88.5%, while with a three-frame camera delay, multiple object tracking accuracy (MOTA) for car drops by 73%. We further demonstrated two attack scenarios using an automotive Ethernet testbed for hardware-in-the-loop validation and the Autoware stack for end-to-end AD simulation, demonstrating the feasibility of the DejaVu attack and its severe impact, such as collisions and phantom braking.

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