On Bit-level Reverse Engineering of Vehicular CAN Bus

Yunlang Cai, Hanxue Shi, Xiaohang Wang, Haoting Shen, Li Lu, Kui Ren

Abstract: The Controller Area Network (CAN) bus is a cornerstone of modern vehicles, orchestrating functions from engine control to auxiliary systems. However, its lack of inherent security measures makes it vulnerable to cyberattacks. Accurately mapping CAN signals with car-control actions is critical for detecting security breaches, as it allows pinpointing potential vulnerabilities exploited to compromise vehicular functions. Despite this, existing CAN reverse engineering methods struggle to achieve bit-level resolution due to the huge search space of unique IDs and payload combinations. To address this challenge, we propose a systematic framework for reverse engineering CAN bus messages, achieving precise mapping of control bits in CAN frames to car-control actions. The framework was validated on Tesla Model 3, Leapmotor C10 and C11, demonstrating its versatility across different vehicle platforms. In particular, it successfully identified 43 car-control actions on the Tesla Model 3, showcasing its extensive coverage. Furthermore, its low resource consumption enables seamless integration into compact platforms like the Raspberry Pi, supporting practical deployment in real-world automotive systems.

External IDs:dblp:conf/dac/CaiSWSLR25