ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage

TL;DR

This paper introduces ASSEMBLAGE-DEEPHISTORY, a comprehensive binary dataset with multi-dimensional metadata, enabling advanced analysis of binary variations, vulnerabilities, and historical build patterns across multiple projects and compilers.

Contribution

It creates a unified, queryable framework combining cross-build diversity, version history, and CVE labels in a large, multi-compiler, multi-year binary dataset with diverse analyses.

Findings

LLM benchmark for binary vulnerability reasoning

Clustering of package versions using embeddings and fuzzy hashes

Bayesian analysis of factors influencing binary similarity

Abstract

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.