Practical Source Code Recovery from Binary Functions Using Anchor-Based Retrieval and LLM Reasoning

2026-07-10

AI Summary

This work addresses the challenging problem of recovering original source code from stripped binary functions, a task where traditional decompilation typically yields only approximate pseudocode. The paper proposes a novel paradigm that replaces pseudocode generation with direct source code retrieval. By extracting anchors such as strings and constants from binaries, the method retrieves candidate functions from a source code corpus and constructs a multimodal representation incorporating assembly instructions, decompiled code, and metadata. A large language model (LLM) is then employed for semantic re-ranking of candidates. The approach integrates Ghidra-based static analysis with an inverted index system and introduces an iterative anchor refinement strategy. Evaluated on a high-quality tcpdump dataset, it achieves 95.2% instruction coverage, and attains 35.5% coverage on general-purpose GitHub repositories, demonstrating effectiveness in both ideal and noisy real-world scenarios.

Abstract

We present a practical pipeline for recovering source code from stripped binary functions by combining reverse engineering, anchor-based source code retrieval, and large language model reasoning. Our binary-to-source-code retrieval method attempts to identify the source function from a source code database, rather than generating approximate decompiled pseudocode. It extracts anchors such as strings, constants, external calls, and available function names using Ghidra, retrieves candidate files via an inverted-index search database, narrows candidates to likely function snippets, and re-ranks them with a large language model (LLM) based on disassembly, decompiled code, and source metadata. Confident matches can also serve as anchors in later passes. In an evaluation backed by our high-fidelity source code database on a stripped, optimized tcpdump binary, our proposed binary-to-source matching method achieves 95.2% assembly instruction coverage. Experiments on a GitHub-based retrieval database showed lower performance with 35.5% instruction coverage on average, mainly due to retrieval misses. These results show that source-level binary recovery excels with high-quality databases and remains a useful tool in noisy environments.

Problem

• source code recovery
• binary functions
• stripped binaries
• reverse engineering
• binary-to-source matching

Innovation

• anchor-based retrieval
• binary-to-source recovery
• large language model reasoning
• reverse engineering
• inverted-index search