Automated Fuzzing of Automotive Control Units
Timothy Werquin, Roos Hubrechtsen, Ashok Thangarajan, Frank Piessens, Jan Tobias Muehlberg
TL;DR
The paper addresses the challenge of security testing for CAN-based automotive ECUs, where traditional methods struggle to automatically detect system changes in response to message injections. It introduces a largely automated fuzzing methodology that combines bug oracles, multiple fuzzing strategies (brute-force, mutation, identify), and a sensor-harness to observe physical ECU outputs, all integrated into the CaringCaribou framework as autoFuzz. The authors define fuzzers, configurations, and oracles, and validate the approach with case studies on VulCAN and automotive instrument clusters, demonstrating rapid vulnerability discovery and substantial speed-ups in reverse-engineering proprietary CAN behavior. The work offers a practical, extensible path for automotive security testing and protocol discovery that can generalize to other cyber-physical systems, backed by an open-source toolchain for replication and extension.
Abstract
Modern vehicles are governed by a network of Electronic Control Units (ECUs), which are programmed to sense inputs from the driver and the environment, to process these inputs, and to control actuators that, e.g., regulate the engine or even control the steering system. ECUs within a vehicle communicate via automotive bus systems such as the Controller Area Network (CAN), and beyond the vehicles boundaries through upcoming vehicle-to-vehicle and vehicle-to-infrastructure channels. Approaches to manipulate the communication between ECUs for the purpose of security testing and reverse-engineering of vehicular functions have been presented in the past, all of which struggle with automating the detection of system change in response to message injection. In this paper we present our findings with fuzzing CAN networks, in particular while observing individual ECUs with a sensor harness. The harness detects physical responses, which we then use in a oracle functions to inform the fuzzing process. We systematically define fuzzers, fuzzing configurations and oracle functions for testing ECUs. We evaluate our approach based on case studies of commercial instrument clusters and with an experimental framework for CAN authentication. Our results show that the approach is capable of identifying interesting ECU states with a high level of automation. Our approach is applicable in distributed cyber-physical systems beyond automotive computing.