HELIOS: Hierarchical Graph Abstraction for Structure-Aware LLM Decompilation

2026-01-21

AI Summary

This work addresses the limitation of current large language models (LLMs) in binary decompilation, where neglecting control flow structure often yields logically inconsistent and non-recompilable code. The authors propose reframing decompilation as a structured reasoning task by introducing a hierarchical control flow graph abstraction that captures basic blocks, successor relationships, and loop/conditional patterns. Combined with tailored prompting and a compiler-in-the-loop feedback mechanism, this approach guides off-the-shelf LLMs to generate structurally sound and recompilable code without requiring model fine-tuning. Evaluated on the HumanEval-Decompile benchmark, the method significantly improves compilability—raising it from 45.0% to 85.2% for Gemini 2.0 and from 71.4% to 89.6% for GPT-4.1 Mini—with further gains exceeding 94% when compiler feedback is incorporated. Functional correctness improves by up to 5.6 percentage points, and the approach demonstrates robust performance across six architectures, including x86, ARM, and MIPS.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Large language models (LLMs) have recently been applied to binary decompilation, yet they still treat code as plain text and ignore the graphs that govern program control flow. This limitation often yields syntactically fragile and logically inconsistent output, especially for optimized binaries. This paper presents \textsc{HELIOS}, a framework that reframes LLM-based decompilation as a structured reasoning task. \textsc{HELIOS} summarizes a binary's control flow and function calls into a hierarchical text representation that spells out basic blocks, their successors, and high-level patterns such as loops and conditionals. This representation is supplied to a general-purpose LLM, along with raw decompiler output, optionally combined with a compiler-in-the-loop that returns error messages when the generated code fails to build. On HumanEval-Decompile for \texttt{x86\_64}, \textsc{HELIOS} raises average object file compilability from 45.0\% to 85.2\% for Gemini~2.0 and from 71.4\% to 89.6\% for GPT-4.1~Mini. With compiler feedback, compilability exceeds 94\% and functional correctness improves by up to 5.6 percentage points over text-only prompting. Across six architectures drawn from x86, ARM, and MIPS, \textsc{HELIOS} reduces the spread in functional correctness while keeping syntactic correctness consistently high, all without fine-tuning. These properties make \textsc{HELIOS} a practical building block for reverse engineering workflows in security settings where analysts need recompilable, semantically faithful code across diverse hardware targets.

Problem

• binary decompilation
• control flow graph
• large language models
• structured reasoning
• program semantics

Innovation

• hierarchical graph abstraction
• structure-aware decompilation
• LLM-based reverse engineering
• compiler-in-the-loop
• control flow representation