Ioannis Angelakopoulos (Boston University), Gianluca Stringhini (Boston University), Manuel Egele (Boston University)

Re-hosting Internet of Things (IoT) firmware can oftentimes be a tedious process, especially when analysts have to intervene with the analysis to ensure further progress. When it comes to Linux-based firmware, one crucial problem that current re-hosting systems face, is that the configuration of the custom kernels used by these systems, significantly deviates from the configuration of the IoT kernel modules used in firmware images. As a consequence, kernel artifacts, such as the memory layout of data structures might differ between the custom kernels and the IoT kernel modules. To analyze the IoT kernel modules within these kernels, the analyst often has to invest significant amount of engineering effort and time to align the offending data structures within the custom kernels. In this paper, we present FirmDiff, an automated binary diffing framework that enables analysts to effectively detect and align the misaligned data structures between the custom kernels produced by the FirmSolo re-hosting framework and the Linux kernel modules in IoT firmware. The goal of FirmDiff is to improve the configuration of FirmSolo’s kernels to closely approximate the configuration of the IoT kernels in the firmware images, such that the IoT kernel modules can be analyzed without errors. We evaluate FirmDiff on a dataset of 10 firmware images with 148 IoT kernel modules that crash during re-hosting with FirmSolo. Using FirmDiff’s findings, we identify 37 misaligned data structures in FirmSolo’s kernels for these images. After aligning the layout of 35 of these data structures, FirmSolo’s refined kernels successfully load 28 previously crashing kernel modules.

View More Papers

NODLINK: An Online System for Fine-Grained APT Attack Detection...

Shaofei Li (Key Laboratory of High-Confidence Software Technologies (MOE), School of Computer Science, Peking University), Feng Dong (Huazhong University of Science and Technology), Xusheng Xiao (Arizona State University), Haoyu Wang (Huazhong University of Science and Technology), Fei Shao (Case Western Reserve University), Jiedong Chen (Sangfor Technologies Inc.), Yao Guo (Key Laboratory of High-Confidence Software Technologies…

Read More

LARMix: Latency-Aware Routing in Mix Networks

Mahdi Rahimi (KU Leuven), Piyush Kumar Sharma (KU Leuven), Claudia Diaz (KU Leuven)

Read More

Secure Multiparty Computation of Threshold Signatures Made More Efficient

Harry W. H. Wong (The Chinese University of Hong Kong), Jack P. K. Ma (The Chinese University of Hong Kong), Sherman S. M. Chow (The Chinese University of Hong Kong)

Read More

It Doesn’t Have to Be So Hard: Efficient Symbolic...

Vaibhav Sharma (University of Minnesota), Navid Emamdoost (University of Minnesota), Seonmo Kim (University of Minnesota), Stephen McCamant (University of Minnesota)

Read More