BinRAG: An RAG-Based Decompilation Framework Fusing Name Prediction and Calling Context

Decompiling stripped binaries to assist human reverse engineers remains a critical yet highly challenging task in software security. Prior work has developed various neural networks and even dedicated large language models (LLMs) to improve function and variable name recovery in decompiled code. Nonetheless, these approaches alone fall short of enabling a generic LLM-based decompilation pipeline that can reliably enhance the readability and semantic clarity of decompiled outputs. In this paper, we propose BinRAG, a retrieval-augmented generation (RAG) based decompilation framework designed to enhance the decompilation of stripped binaries. Building upon name prediction models, BinRAG features three novel designs: (1) it first utilizes name prediction models to transform raw decompiled outputs into enriched, source-like queries for RAG retrieval; (2) it further enhances these queries using a fine-tuned specialized LLM conditioned on the predicted variable names, enabling more accurate retrieval from a curated example database; (3) it then integrates these retrieved examples with the target’s calling context, employing a general-purpose LLM to synthesize high-fidelity, human-readable decompiled code. Evaluation on 3,200 functions from real-world software repositories demonstrates that BinRAG improves readability by 8.4% over standard RAG and semantic precision by 31.2% over the next-best prior baseline. Our results show that BinRAG effectively scales to large codebases and significantly reduces manual effort in reverse engineering tasks.