[2604.03750] CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering

Computer Science > Cryptography and Security arXiv:2604.03750 (cs) [Submitted on 4 Apr 2026] Title:CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering Authors:Baicheng Chen, Yu Wang, Ziheng Zhou, Xiangru Liu, Juanru Li, Yilei Chen, Tianxing He View a PDF of the paper titled CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering, by Baicheng Chen and 6 other authors View PDF HTML (experimental) Abstract:Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities. It supports critical tasks such as vulnerability discovery and malware analysis. Despite its importance, RE remains labor-intensive and requires substantial expertise, making large language models (LLMs) a potential solution for automating the process. However, their capabilities for RE remain systematically underexplored. To address this gap, we study the cryptographic binary RE capabilities of LLMs and introduce \textbf{CREBench}, a benchmark comprising 432 challenges built from 48 standard cryptographic algorithms, 3 insecure crypto key usage scenarios, and 3 difficulty levels. Each challenge follows a Capture-the-Flag (CTF) RE challenge, requiring the model to analyze the underlying cryptographic logic and recover the correct input. We design an evaluation framework comprising four sub-tasks, from algorithm identification to correct flag reco...