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CrypFormBench: Benchmarking Formal Analysis Capability of Large Language Models for Cryptographic Schemes

arXiv Security Archived Jun 25, 2026 ✓ Full text saved

arXiv:2606.25561v1 Announce Type: new Abstract: Manual formal analysis of cryptographic schemes is labor-intensive and requires substantial expertise. While model-checking tools (e.g., Scyther and Tamarin) and computational-security tools (e.g., CryptoVerif and EasyCrypt) improve the automation of security proofs, they still rely on experts to abstract schemes and write tool-specific formal descriptions. Large language models (LLMs) are a promising alternative, but their effectiveness in this do

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    Computer Science > Cryptography and Security [Submitted on 24 Jun 2026] CrypFormBench: Benchmarking Formal Analysis Capability of Large Language Models for Cryptographic Schemes Zhaoxuan Li, Qionglu Zhang, Hengyuan Liu, Xiaoyan Gu, Xianhui Lu, Hongbo Liu, Bingzheng Wang, Haihui Fan, Ziming Zhao, Rui Zhang, Li Zhou Manual formal analysis of cryptographic schemes is labor-intensive and requires substantial expertise. While model-checking tools (e.g., Scyther and Tamarin) and computational-security tools (e.g., CryptoVerif and EasyCrypt) improve the automation of security proofs, they still rely on experts to abstract schemes and write tool-specific formal descriptions. Large language models (LLMs) are a promising alternative, but their effectiveness in this domain remains unexplored due to the absence of standardized evaluation methodologies. To fill this gap, we introduce CrypFormBench (C.F.B for short), a comprehensive benchmark jointly covering symbolic and computational security to evaluate five core LLM capabilities: interpretation, generation, completion, transformation, and correction. It comprises 700 instances spanning 677 schemes, 7 mainstream formal verifier languages, and 160 security properties. The evaluation of 9 state-of-the-art LLMs reveals that most of them perform well on interpretation and completion, given their code-awareness advantages, but struggle with generation, transformation, and correction. Overall, their performance remains limited, with Claude-3.5 achieving the highest score at 48.7 out of 100. We further provide practical guidance, e.g., few-shot prompting, Pass@K sampling, and lightweight fine-tuning, to mitigate the executability bottleneck and improve tool-usable outputs. Taken together, our benchmark and analyses offer a grounded view of current progress and concrete directions toward reliable LLM-assisted formal cryptographic analysis. Comments: 23 pages, 17 figures, FSE 2026 Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.25561 [cs.CR]   (or arXiv:2606.25561v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.25561 Focus to learn more Related DOI: https://doi.org/10.1145/3808184 Focus to learn more Submission history From: Zhaoxuan Li [view email] [v1] Wed, 24 Jun 2026 08:37:38 UTC (9,514 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
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    ◬ AI & Machine Learning
    Published
    Jun 25, 2026
    Archived
    Jun 25, 2026
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