2024 · Preprint
Exploring the Efficacy of Large Language Models (GPT-4) in Binary Reverse Engineering
The paper evaluates GPT-4 for binary reverse engineering through interpretation of human-written and decompiled code, from basic code explanation to malware analysis.
Scope and limitations
The evaluation covers GPT-4, human-written and decompiled code, basic code interpretation, and more complex malware analysis.
Why it fits this collection
It directly studies the use of a language model to interpret code in binary reverse engineering.
Publication and review details
- Venue
- arXiv
- Status
- Preprint
- Publication / posting date
- June 9, 2024
- Date source
- First arXiv submission date.
- First arXiv submission
- June 9, 2024
- arXiv
- 2406.06637
- Reviewed arXiv version
- v1
The date and venue come from the arXiv record.
Sources reviewed
Reviewed against the paper and its primary sources. Cite the original authors using the linked paper or BibTeX.