Modern vehicles are controlled by an on-board network of ECUs (Electronic Control Units), which are specially designed computers that contain tightly tailored and customized software. Especially the trends for ECU connectivity and for semi-autonomous driver assistance functions may have an impact on passenger safety and require thorough security assessments, yet the ECU divergence strains those assessments. We therefore propose an easily automated, quantitative, probabilistic method and metric based on ECU development data and software flash images for the attack surface and vulnerability assessment automation. Our method and metric is designed for the integration into an (iterative) engineering process and the facilitation of code reviews and other security assessments, such as penetration tests. The automotive attack surface comprises especially internal communication interfaces, including diagnosis protocols, external and user-accessible interfaces, such as USB sockets, as well as low-level hardware interfaces. Some exemplary indicators for the vulnerability are access restrictions, casing tamper-resistance, code size, previously found vulnerabilities; strictness of compilers, frameworks and application binary interfaces; conducted security audits and deployed exploit mitigation techniques. This paper's main contributions are I) a method and a metric for collecting attack surface and predicting the engineering effort for a code injection exploit from ECU development data and II) an application of our metric and method into our graph-based security assessment.
['Martin Salfer', 'Claudia Eckert']
Attack surface and vulnerability assessment of automotive Electronic Control Units