theelderemo/pentesting-explanations

一个高质量的监督微调数据集，专注于渗透测试专业知识、红队技巧以及新颖漏洞研究和零日推理。数据集的结构设计旨在教授模型如何像进攻性安全从业者一样思考，而不仅仅是回忆标签或技术名称。数据集的长期目标是训练能够进行真正对抗性推理的模型，包括从陌生代码中形成假设、数据流跟踪、跨补丁历史的变体搜索以及利用原始构造逻辑等。数据集包含多个来源的数据，每个来源存储在不同的parquet文件中，便于用户根据需要选择。

A high-quality supervised fine-tuning dataset for penetration testing expertise, red team tradecraft, and novel vulnerability research and zero-day reasoning. The dataset is structured to teach models how to think like offensive security practitioners, not merely recall labels or technique names. The long-term goal of this dataset is to train models capable of genuine adversarial reasoning: hypothesis formation from unfamiliar code, data-flow tracing, variant hunting across patch history, and exploit primitive construction logic. The dataset includes data from multiple sources, each stored in separate parquet files, allowing users to select exactly which sources they want to include in training.