Moye: A Wallbreaker for Monolithic Firmware

As embedded devices become increasingly popular, monolithic firmware, known for its execution efficiency and simplicity, is widely used in resource-constrained devices. Different from ordinary firmware, the monolithic firmware image is packed without the file that indicates its format, which challen...

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Bibliographic Details
Published in:Proceedings / International Conference on Software Engineering pp. 116 - 128
Main Authors: Huang, Jintao, Yang, Kai, Wang, Gaosheng, Shi, Zhiqiang, Pan, Zhiwen, Lv, Shichao, Sun, Limin
Format: Conference Proceeding
Language:English
Published: IEEE 26.04.2025
Subjects:
Codes
Function Identification
Image segmentation
Microprogramming
Monolithic Firmware
Registers
Reverse engineering
Robustness
Software engineering
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ISSN:1558-1225
Online Access:Get full text
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Summary:As embedded devices become increasingly popular, monolithic firmware, known for its execution efficiency and simplicity, is widely used in resource-constrained devices. Different from ordinary firmware, the monolithic firmware image is packed without the file that indicates its format, which challenges the reverse engineering of monolithic firmware. Function identification is the prerequisite of monolithic firmware's analysis. Prior works on function identification are less effectiveness when applied to monolithic firmware due to their heavy reliance on file formats. In this paper, we propose Moye, a novel method to identify functions in monolithic firmware. We leverage the important insight that the use of registers must conform to some constraints. In particular, our approach segments the firmware, locate code sections and output the instructions. We use a masked language model to learn hiding relationships among the instructions to identify the function boundaries. We evaluate Moye using 1,318 monolithic firmware images, including 48 samples collected from widely used devices. The evaluation demonstrates that our approach significantly outperforms current works, achieving a precision greater than 98 % and a recall rate greater than 97 % across most datasets, showing robustness to complicated compilation options.
ISSN:1558-1225
DOI:10.1109/ICSE55347.2025.00053