EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild
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 hi...
| Authors | Yiming Fan, Jun Yeon Won, Ding Zhu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 2 April 2026 |
| Last updated at source | 5 October 2026 |
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