首个融合跨编译、历史版本与漏洞标签的二进制数据集，支持精准分析漏洞演化。
ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage
- 构建统一框架，存储二进制的编译环境、源码、漏洞函数等元数据。
- 覆盖73,610个二进制文件，涵盖248个开源项目，跨编译器与多平台。
- 适用于漏洞挖掘、模型泛化性测试及软件演化研究者。
现有二进制语料库通常仅涵盖单一或两个维度的二进制差异：要么提供跨编译器构建但无时间轴，要么仅对单次构建的二进制标注CVE信息。目前尚无数据集能将跨构建多样性、跨版本历史与CVE标签整合为可查询结构。我们提出ASSEMBLAGE-DEEPHISTORY，将这些维度统一到一个框架中，使每个二进制的编译上下文、源代码、漏洞函数及包版本均作为第一类元数据存储。该数据集包含73,610个二进制文件，覆盖248个开源项目，在Linux和Windows上使用GCC、Clang、MSVC在多个优化级别下编译，涵盖多年历史构建。每个二进制均被索引至数据库，关联其源码、函数、调试信息、变体构建、历史版本及漏洞函数。三项分析验证其价值：(1) 三阶段大语言模型基准测试（识别、策略引导检测、跨构建迁移），检验LLM是否推理二进制漏洞而非模式匹配构建特有特征；(2) 对比MalConv嵌入、jTrans函数嵌入与TLSH模糊哈希，量化同包版本在各空间中的聚类情况；(3) 通过贝叶斯回归分解二进制相似性，分析时间距离、文件变更与提交数的贡献度。
原文摘要 · Abstract (English)
Existing binary corpora typically capture only one or two axes of binary variation: they either provide cross-compiler builds without a temporal axis, or CVE labels for single-build binaries. None combine cross-build diversity, cross-version history, and CVE labels into a queryable structure. We present ASSEMBLAGE-DEEPHISTORY, which consolidates these dimensions into a unified framework where every binary's compilation context, source code, vulnerable functions, and package version are stored as first-class metadata. ASSEMBLAGE-DEEPHISTORY comprises 73,610 binaries spanning 248 open-source projects, compiled across GCC, Clang, and MSVC at multiple optimization levels on Linux and Windows, with multi-year historical builds. Each binary is indexed in a database that links it to its source code, functions, debug info, variant builds, historical versions, and vulnerable functions. Three analyses demonstrate this structure's value: (1) a three-stage LLM benchmark (recognition, strategy-guided detection, and cross-build transfer) to test whether LLMs reason about binary vulnerabilities or pattern-match on build-specific artifacts; (2) a comparison of MalConv embeddings, jTrans function embeddings, and TLSH fuzzy hashes quantifying how same-package versions cluster in each space; and (3) a Bayesian regression decomposing binary similarity into contributions from temporal distance, file changes, and commits.
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