Lifting assembly to intermediate representation: A novel approach leveraging compilers

Abstract

Translating low-level machine instructions into higher-level intermediate language (IL) is one of the central steps in many binary analysis and instrumentation systems. Existing systems build such translators manually. As a result, it takes a great deal of effort to support new architectures. Even for widely deployed architectures, full instruction sets may not be modeled, e.g., mature systems such as Valgrind still lack support for AVX, FMA4 and SSE4.1 for x86 processors. To overcome these difficulties, we propose a novel approach that leverages knowledge about instruction set semantics that is already embedded into modern compilers such as GCC. In particular, we present a learning-based approach for automating the translation of assembly instructions to a compiler's architecture-neutral IL. We present an experimental evaluation that demonstrates the ability of our approach to easily support many architectures (x86, ARM and AVR), including their advanced instruction sets. Our implementation is available as open-source software.