My coauthors and I will be presenting the paper "EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild" at the 4th EAI International Conference on Security and Privacy in Cyber-Physical Systems and Smart Vehicles (SmartSP'26) in November. Below is a preview of the 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 type of binary, 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 substantial generalization gaps. Our results show that robustness to low- and mid-level binary variations does not generalize to high-level semantic differences, underscoring a critical blind spot in current BFSD evaluation practices.