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Automatic vulnerability detection on C/C++ source code has benefitted fromthe introduction of machine learning to the field, with many recentpublications targeting this combination. In contrast, assembly language ormachine code artifacts receive less attention, although there are compellingreasons to study them. They are more representative of what is executed, moreeasily incorporated in dynamic analysis, and in the case of closed-source code,there is no alternative.  We evaluate the representative capability of assembly language compared toC/C++ source code for vulnerability detection. Furthermore, we investigate therole of call graph context in detecting function-spanning vulnerabilities.Finally, we verify whether compiling a benchmark dataset compromises anexperiment's soundness by inadvertently leaking label information.  We propose ROMEO, a publicly available, reproducible and reusable binaryvulnerability detection benchmark dataset derived from the synthetic Juliettest suite. Alongside, we introduce a simple text-based assembly languagerepresentation that includes context for function-spanning vulnerabilitydetection and semantics to detect high-level vulnerabilities. It is constructedby disassembling the .text segment of the respective binaries.  We evaluate an x86 assembly language representation of the compiled dataset,combined with an off-the-shelf classifier. It compares favorably tostate-of-the-art methods, including those operating on the full C/C++ code.Including context information using the call graph improves detection offunction-spanning vulnerabilities. There is no label information leaked duringthe compilation process.

ROMEO: Exploring Juliet through the Lens of Assembly Language