Automated Fuzzing of Automotive Control Units

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 cyberphysical systems beyond automotive computing.

Cybersecurity Testing for Automotive Domain: A Survey

“…The security properties of the ECU were also tested by fuzzy data injection of the display ECU [ 47 ]. Werquin et al [ 48 ] developed a sensor harness to facilitate automated detection of the system’s behavior, which has significant advantages over previous manual testing approaches. Furthermore, the method has been integrated into the open-source security testing tool CaringCaribou.…”

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

“…By subjecting the system to such inputs, fuzz testing aims to identify potential security weaknesses, such as buffer overflows or input validation errors [158][159][160][161]. Fuzz testing can be automated and significantly enhances the security of automotive systems by identifying and fixing software vulnerabilities [162][163][164][165].…”