# FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs

> FoC uses FoC-BinLLM to summarize cryptographic functions in stripped binaries and FoC-Sim to retrieve similar implementations. It evaluates both models and applications to virus analysis and 1-day vulnerability detection.

- Authors: Xiuwei Shang; Guoqiang Chen; Shaoyin Cheng; Shikai Guo; Yanming Zhang; Weiming Zhang; Nenghai Yu
- Venue: arXiv
- Status: Preprint
- Publication / posting date: March 27, 2024
- Date source: First arXiv submission date.
- First arXiv submission: March 27, 2024
- arXiv: 2403.18403
- Reviewed arXiv version: v3
- Sources reviewed: 2026-11-06
- Added to the catalog: 2026-11-06
- Original paper: <https://arxiv.org/abs/2403.18403>
- PDF: <https://arxiv.org/pdf/2403.18403>
- BibTeX: <https://awesomerea.org/papers/citations/paper-foc.bib>
- Catalog page: <https://awesomerea.org/papers/paper-foc/>
- Record JSON: <https://awesomerea.org/papers/paper-foc/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/papers/paper-foc/index.md)

## Scope and limitations

Evaluation uses a constructed cryptographic binary dataset and automatically generated semantic labels. It reports a 14.61% higher ROUGE-L score than ChatGPT for FoC-BinLLM and 52% higher Recall@1 than previous best methods for FoC-Sim.

## Why it fits this collection

It addresses reverse engineering of cryptographic functions in stripped binaries through semantic analysis and similarity retrieval.

## Publication and review notes

The date and venue come from the arXiv record.

## Primary sources

1. <https://arxiv.org/abs/2403.18403>

## 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.