# ReSym: Harnessing LLMs to Recover Variable and Data Structure Symbols from Stripped Binaries

> Combines two fine-tuned language models with Prolog-based cross-function reasoning to infer local-variable symbols and user-defined data structures from stripped-binary decompilation.

- Authors: Danning Xie; Zhuo Zhang; Nan Jiang; Xiangzhe Xu; Lin Tan; Xiangyu Zhang
- Venue: ACM CCS 2024
- Status: Conference paper
- Publication / posting date: December 2, 2024
- Date source: ACM-deposited Crossref print-publication date; the October 14–18 CCS 2024 conference week is not used as the publication date.
- DOI: 10.1145/3658644.3670340
- Sources reviewed: 2026-10-17
- Added to the catalog: 2026-10-16
- Original paper: <https://doi.org/10.1145/3658644.3670340>
- PDF: <https://www.cs.purdue.edu/homes/lintan/publications/resym-ccs24.pdf>
- Code: <https://github.com/lt-asset/resym>
- BibTeX: <https://awesomerea.org/papers/citations/paper-resym.bib>
- Catalog page: <https://awesomerea.org/papers/paper-resym/>
- Record JSON: <https://awesomerea.org/papers/paper-resym/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-resym/index.md)

## Scope and limitations

The method depends on underlying decompilation and learned predictions over C/C++ data. Cross-checking reduces conflicting predictions but does not prove that recovered names or layouts match the unavailable source. The public artifact includes models, processed data, and evaluation code.

## Why it fits this collection

A concrete hybrid approach for grounding LLM-generated reverse-engineering annotations in program analysis.

## Publication and review notes

ACM metadata records print publication on 2024-12-02 and online publication on 2024-12-09, pages 4554–4568. The author-hosted published PDF identifies the event as CCS ’24, held October 14–18, 2024 in Salt Lake City. The DOI, title, six authors and venue agree with the publisher record and the official author artifact.

## Primary sources

1. <https://doi.org/10.1145/3658644.3670340>
2. <https://api.crossref.org/works/10.1145/3658644.3670340>
3. <https://www.cs.purdue.edu/homes/lintan/publications/resym-ccs24.pdf>
4. <https://github.com/lt-asset/resym>
5. <https://github.com/lt-asset/resym/blob/437b4acac21a5a60edecf0c4423f7172f991271f/README.md>
6. <https://dnxie.github.io/>
7. <https://zenodo.org/records/13917253>

## Related entries

- [ReSym (CCS 2024)](https://awesomerea.org/resources/resym-paper/index.md) · Curated resource

## About this entry

Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.