Function Renaming in Reverse Engineering of Embedded Device Firmware with ChatGPT

Firmware reverse engineering is crucial for exposing internal mechanisms and identifying security vulnerabilities in embedded systems. While reconstructing the structural components of code is generally feasible, the absence of function names greatly complicates efforts to analyze and comprehend firmware logic. Motivated by the demonstrated code generation capabilities of large language models (LLMs), this paper investigates their potential to automate function renaming. We introduce FirmNamer, a prototype system designed to streamline the labor-intensive process of ana- lyzing decompiled code and assigning meaningful function names. FirmNamer accomplishes this by dynamically constructing LLM prompts based on extracted function code and contextual informa- tion. Extensive evaluation shows that FirmNamer achieves superior performance in function renaming, obtaining a functional precision of 86.6% and a semantic precision of 49%, thereby surpassing existing state-of-the-art approaches such as DeGPT, DEBIN, NFRE, NERO, and SYMLM.