ToolPhet: Inference of Compiler Provenance From Stripped Binaries With Emerging Compilation Toolchains

Abstract

Identifying compiler toolchain provenance serves as a basis for both benign and malicious binary analyses. A wealth of prior studies mostly focuses on the inference of a popular compiler toolchain for C and C++ languages from stripped binaries that are built with GCC or clang. Lately, the popularity of an emerging compiler is on the rise such as Rust, Go, and Nim programming languages that complement the downsides of C and C++ (e.g., security), which little has been explored on them. The main challenge arises when applying previous inference techniques for toolchain provenance because some emerging compilation toolchains adopt the same backend of traditional compilers. In this paper, we propose ToolPhet, an effective end-to-end BERT-based system for deducing the provenance of both traditional and emerging compiler toolchains. To this end, we thoroughly study the characteristics of both an emerging toolchain and an executable binary that is generated by that toolchain. We introduce two separate downstream tasks for the compiler toolchain inference with a (BERT-based) fine-tuning process, which produces 1) a toolchain classification model; and 2) a binary code similarity detection model. Our findings show that the classification model 1) may not suffice when producing a binary with the existing backend like Nim, which we adopt the detection model 2) that can infer underlying code semantics. We evaluate ToolPhet with the previous work including one signature-based tool and four machine-learning-based approaches, demonstrating its effectiveness by achieving higher F1 score s with the binaries compiled with emerging compilation toolchains.

Keywords

• Compiler inference
• binary analysis
• BERT
• classification model
• similarity model