SCRIBE: Practical Static Binary Patching via Binary-Aware Recompilation of Decompiled Code

• URL: http://arxiv.org/abs/2605.02121v1
• Date: Mon, 04 May 2026 00:59:29 GMT
• Title: SCRIBE: Practical Static Binary Patching via Binary-Aware Recompilation of Decompiled Code
• Abstract summary: We present SCRIBE, a patching framework that handles syntactic and semantic issues in decompiled code.<n> SCRIBE's novel "binary-aware" recompilation approach repairs semantic inaccuracies in decompiler output.<n>We show that, using SCRIBE, it is possible to patch 13 of 14 real-world CVEs without access to the original source code.
• Score: 9.623962750101006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When source code or the original toolchain is unavailable, patching binaries is difficult because it requires editing low-level assembly code directly. As an alternative, one can decompile the binary, apply the patch at the source level, and then recompile the modified code. However, as this paper demonstrates, this workflow is hindered by pervasive syntactic and semantic inaccuracies in the output of modern decompilers, many of which prior work has overlooked. To address these challenges, we present SCRIBE, a patching framework that handles syntactic and semantic issues in decompiled code, improving both recompilation success and correctness. SCRIBE's novel "binary-aware" recompilation approach repairs semantic inaccuracies in decompiler output by leveraging information extracted directly from the original binary. In our evaluation, SCRIBE resolved approximately 81% of previously incorrect functions produced by the Hex-Rays decompiler, demonstrating the effectiveness of its approach. Moreover, we show that, using SCRIBE, it is possible to patch 13 of 14 real-world CVEs without access to the original source code and without performing any manual binary editing. To further validate our findings, we conducted a user study with 18 participants. Using SCRIBE, participants achieved 100% patching success, compared to 3.7% without it. Finally, we asked three large language models to generate source-level patches via SCRIBE; all three achieved 100% success when using the framework, demonstrating its potential to enable fully automated patching. Overall, these results indicate that SCRIBE makes source-level patching of binaries accessible and reliable, even without access to the original source.

Related papers

• Decaf: Improving Neural Decompilation with Automatic Feedback and Search [64.02615739798277]
  High-level syntax, identifiers, and custom data types are generally lost as the compiler translates human-readable code to low-level machine code.<n>Generative AI models are a natural fit for reconstructing high-level syntax, identifiers, and types.<n>We argue that compiler feedback can be used to dramatically improve the semantic correctness of neural decompiler outputs via search.
  arXiv  Detail & Related papers  (2026-05-12T04:21:26Z)
• Constraint-Guided Multi-Agent Decompilation for Executable Binary Recovery [48.82204413712889]
  Decompilation is essential for security analysis, malware reverse engineering, and legacy software maintenance.<n>We present a multi-agent framework that transforms decompiled code into re-executable source through Multi-level Constraint-Guided Decompilation (MCGD)<n>Our framework achieves 84-97% re-executability, improving baseline decompiler output by 28-89 percentage points.
  arXiv  Detail & Related papers  (2026-04-27T01:28:11Z)
• PCodeTrans: Translate Decompiled Pseudocode to Compilable and Executable Equivalent [8.576619291429969]
  PCodeTrans bridges the gap between decompilation, recompilation, and rigorous function-level dynamic validation.<n>It achieves 100% function-level compilability on unstripped binaries alongside 99.55% and 99.89% test-validated behavioral consistency.
  arXiv  Detail & Related papers  (2026-03-16T05:54:24Z)
• Context-Guided Decompilation: A Step Towards Re-executability [50.71992919223209]
  Binary decompilation plays an important role in software security analysis, reverse engineering and malware understanding.<n>Recent advances in large language models (LLMs) have enabled neural decompilation, but the generated code is typically only semantically plausible.<n>We propose ICL4Decomp, a hybrid decompilation framework that leverages in-context learning (ICL) to guide LLMs toward generating re-executable source code.
  arXiv  Detail & Related papers  (2025-11-03T17:21:39Z)
• SALT4Decompile: Inferring Source-level Abstract Logic Tree for LLM-Based Binary Decompilation [17.58664677898224]
  saltm is a novel binary decompilation method that abstracts stable logical features between binary and source code.<n>saltm is highly effective in recovering the logic of the source code, significantly outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-09-18T05:57:15Z)
• Decompiling Smart Contracts with a Large Language Model [51.49197239479266]
  Despite Etherscan's 78,047,845 smart contracts deployed on (as of May 26, 2025), a mere 767,520 ( 1%) are open source.<n>This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode.<n>We introduce a pioneering decompilation pipeline that transforms bytecode into human-readable and semantically faithful Solidity code.
  arXiv  Detail & Related papers  (2025-06-24T13:42:59Z)
• D-LiFT: Improving LLM-based Decompiler Backend via Code Quality-driven Fine-tuning [49.16469288280772]
  Decompilers reconstruct human-readable source code from binaries.<n>Despite recent advances, their outputs often suffer from syntactic and semantic errors and remain difficult to read.<n>With the advent of large language models (LLMs), researchers began to explore the potential of LLMs to refine decompiler output.<n>We present D-LIFT, an enhanced decompiler-LLM pipeline with fine-tuned reinforcement learning.
  arXiv  Detail & Related papers  (2025-06-11T19:09:08Z)
• Decompile-Bench: Million-Scale Binary-Source Function Pairs for Real-World Binary Decompilation [18.28488002922524]
  Decompile-Bench is the first open-source dataset comprising two million binary-source function pairs condensed from 100 million collected function pairs.<n>For the evaluation purposes, we developed a benchmark Decompile-Bench-Eval including manually crafted binaries from the well-established HumanEval and MBPP.<n>We find that fine-tuning with Decompile-Bench causes a 20% improvement over previous benchmarks in terms of the re-executability rate.
  arXiv  Detail & Related papers  (2025-05-19T03:34:33Z)
• ReF Decompile: Relabeling and Function Call Enhanced Decompile [50.86228893636785]
  The goal of decompilation is to convert compiled low-level code (e.g., assembly code) back into high-level programming languages.<n>This task supports various reverse engineering applications, such as vulnerability identification, malware analysis, and legacy software migration.
  arXiv  Detail & Related papers  (2025-02-17T12:38:57Z)
• BinGo: Identifying Security Patches in Binary Code with Graph Representation Learning [19.22004583230725]
  We propose BinGo, a new security patch detection system for binary code. BinGo consists of four phases, namely, patch data pre-processing, graph extraction, embedding generation, and graph representation learning. Our experimental results show BinGo can achieve up to 80.77% accuracy in identifying security patches between two neighboring versions of binary code.
  arXiv  Detail & Related papers  (2023-12-13T06:35:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.