一句话：论文提出 BinPool，一个面向二进制安全分析的漏洞数据集，包含真实漏洞及其补丁配对。
该论文关注二进制安全分析中机器学习漏洞发现的数据基础问题：训练有效模型需要大规模、语义多样、真实世界的漏洞样本，并且每个程序应同时包含漏洞版本与修复版本。然而此类数据集收集成本高。作者指出，二进制代码漏洞发现领域的已有数据集存在公开性差、语义多样性不足、漏洞人为引入，或依赖静态分析器收集而导致错误标注等问题。标题表明论文提出 BinPool，旨在为二进制安全分析提供漏洞数据集。摘要未具体描述其构建流程、数据规模与评测结果，因此贡献主要体现为面向该任务的数据资源建设。
The development of machine learning techniques for discovering software vulnerabilities relies fundamentally on the availability of appropriate datasets. The ideal dataset consists of a large and diverse collection of real-world vulnerabilities, paired so as to contain both vulnerable and patched versions of each program. Naturally, collecting such datasets is a laborious and time-consuming task. Within the specific domain of vulnerability discovery in binary code, previous datasets are either publicly unavailable, lack semantic diversity, involve artificially introduced vulnerabilities, or were collected using static analyzers, thereby themselves containing incorrectly labeled example programs.
@inproceedings{paperbot9188,
title = {BinPool: A Dataset of Vulnerabilities for Binary Security Analysis},
author = {Sima Arasteh and Georgios Nikitopoulos and Wei-Cheng Wu and Nicolaas Weideman and Aaron Portnoy and Mukund Raghothaman and Christophe Hauser},
booktitle = {FSE},
year = {2025},
doi = {10.1145/3696630.3728606}
}