CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering

This paper by Baicheng Chen and six co-authors, accepted at COLM 2026 and revised in August 2026, studies how well large language models perform reverse engineering of cryptographic binaries, a task central to vulnerability discovery and malware analysis that has been largely unexplored systematically. The authors introduce CREBench, a benchmark of 432 challenges built from 48 standard cryptographic algorithms, 3 insecure crypto key usage scenarios, and 3 difficulty levels, each framed as a Capture-the-Flag reverse engineering challenge requiring the model to analyze the underlying cryptographic logic and recover the correct input. They design an evaluation framework with four sub-tasks, spanning algorithm identification through correct flag recovery, and test eight frontier LLMs. GPT-5.4 is the best performer at 64.03 out of 100, recovering the flag in 59 percent of challenges, while a strong human expert baseline scores 92.19, showing humans retain an advantage on cryptographic reverse engineering tasks. The code and dataset are publicly available. 

https://arxiv.org/abs/2604.03750

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