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

BGP-iSec: Improved Security of Internet Routing Against Post-ROV Attacks

Cameron Morris (University of Connecticut), Amir Herzberg (University of Connecticut), Bing Wang (University of Connecticut), Samuel Secondo (University of Connecticut)

Read More

PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the...

Man Zhou (Huazhong University of Science and Technology), Shuao Su (Huazhong University of Science and Technology), Qian Wang (Wuhan University), Qi Li (Tsinghua University), Yuting Zhou (Huazhong University of Science and Technology), Xiaojing Ma (Huazhong University of Science and Technology), Zhengxiong Li (University of Colorado Denver)

Read More

SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systems

Guangke Chen (ShanghaiTech University), Yedi Zhang (National University of Singapore), Fu Song (Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences)

Read More