Extended Abstract: Towards Reliable and Scalable Linux Kernel CVE Attribution in Automated Static Firmware Analyses

In vulnerability assessments, software component-based CVE attribution is a common method to identify possibly vulnerable systems at scale. However, such version-centric approaches yield high false-positive rates for binary distributed Linux kernels in firmware images. Not filtering included vulnerable components is a reason for unreliable matching, as heterogeneous hardware properties, modularity, and numerous development streams result in a plethora of vendor-customized builds. To make a step towards increased result reliability while retaining scalability of the analysis method, we enrich version-based CVE matching with kernel-specific build data from binary images using automated static firmware analysis. In a case study with 127 router firmware images, we show that in comparison to naive version matching, our approach identifies 68% of all version CVE matches as false-positives and reliably removes them from the result set. For 12% of all matches it provides additional evidence of issue applicability.