mlsanityData to 7 Oct 2026

Can We Generate Shellcodes via Natural Language? An Empirical Study

arXiv 2202.03755 ↗First posted 8 Feb 2022cs.SE cs.LGcited by 0

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Abstract (arXiv)

Writing software exploits is an important practice for offensive security analysts to investigate and prevent attacks. In particular, shellcodes are especially time-consuming and a technical challenge, as they are written in assembly language. In this work, we address the task of automatically generating shellcodes, starting purely from descriptions in natural language, by proposing an approach based on Neural Machine Translation (NMT). We then present an empirical study using a novel dataset (Shellcode_IA32), which consists of 3,200 assembly code snippets of real Linux/x86 shellcodes from public databases, annotated using natural language. Moreover, we propose novel metrics to evaluate the accuracy of NMT at generating shellcodes. The empirical analysis shows that NMT can generate assembly code snippets from the natural language with high accuracy and that in many cases can generate entire shellcodes with no errors.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 37 of its 97 references that are in the map. First posted on arXiv 8 Feb 2022.

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37 of 97 references.

References · 37

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