[2604.01554] EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild

Computer Science > Cryptography and Security arXiv:2604.01554 (cs) [Submitted on 2 Apr 2026] Title:EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild Authors:Yiming Fan (1), Jun Yeon Won (1), Ding Zhu (1), Melih Sirlanci (1), Mahdi Khalili (1), Carter Yagemann (1) ((1) The Ohio State University) View a PDF of the paper titled EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild, by Yiming Fan (1) and 5 other authors View PDF HTML (experimental) Abstract:Binary Function Similarity Detection (BFSD) is a core problem in software security, supporting tasks such as vulnerability analysis, malware classification, and patch provenance. In the past few decades, numerous models and tools have been developed for this application; however, due to the lack of a comprehensive universal benchmark in this field, researchers have struggled to compare different models effectively. Existing datasets are limited in scope, often focusing on a narrow set of transformations or types of binaries, and fail to reflect the full diversity of real-world applications. We introduce EXHIB, a benchmark comprising five realistic datasets collected from the wild, each highlighting a distinct aspect of the BFSD problem space. We evaluate 9 representative models spanning multiple BFSD paradigms on EXHIB and observe performance degradations of up to 30% on firmware and semantic datasets compared to standard settings, revealing subst...