Automatically Inferring Image Bases of ARM32 Binaries

Reverse engineering tools rely on the critical image base value for tasks such as correctly mapping code into virtual memory for an emulator or accurately determining branch destinations for a disassembler. However, binaries are often stripped and therefore, do not explicitly state this value. Currently available solutions for calculating this essential value generally require user input in the form of parameter configurations or manual binary analysis, thus these methods are limited by the experience and knowledge of the user. In this thesis, we propose a user-independent solution for determining the image base of ARM32 binaries and describe our implementation. Our solution makes use of features present in all ARM32 binaries, utilizing statistical, structural, and semantical filtration to automatically calculate the image base value. We implemented our tool in 335 lines of Python. We tested our tool on 20 stripped binaries, and it successfully determined the image bases of each binary