EXHIB：一个用于函数相似性在真实世界中真实和多样评估的基准
EXHIB: A Benchmark for Realistic and Diverse Evaluation of Function Similarity in the Wild
AI总结：
本文提出EXHIB基准，通过五个真实世界数据集评估函数相似性检测问题，发现现有模型在处理固件和语义数据时性能下降30%，揭示了当前评估方法的局限性。
摘要：
二进制函数相似性检测（BFSD）是软件安全中的核心问题，支持漏洞分析、恶意软件分类和补丁溯源等任务。尽管过去几十年发展了众多模型和工具，但因缺乏全面的通用基准，研究人员难以有效比较不同模型。现有数据集范围有限，通常聚焦于狭窄的转换或二进制类型，无法反映真实世界应用的多样性。我们引入EXHIB，包含五个从真实世界收集的真实数据集，每个数据集突出BFSD问题空间的不同方面。我们在EXHIB上评估了9个代表性模型，涵盖多种BFSD范式，发现固件和语义数据集的性能下降高达30%，揭示了显著的泛化差距。我们的结果表明，对低级和中级二进制变化的鲁棒性不适用于高级语义差异，凸显了当前BFSD评估实践中的关键盲点。
英文摘要：
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 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.
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