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A shellcode is a small piece of code and it is executed to exploit a softwarevulnerability, which allows the target computer to execute arbitrary commandsfrom the attacker through a code injection attack. Similar to the purpose ofautomated vulnerability generation techniques, the automated generation ofshellcode can generate attack instructions, which can be used to detectvulnerabilities and implement defensive measures. While the automatedsummarization of shellcode can help users unfamiliar with shellcode and networkinformation security understand the intent of shellcode attacks. In this study,we propose a novel approach DualSC to solve the automatic shellcode generationand summarization tasks. Specifically, we formalize automatic shellcodegeneration and summarization as dual tasks, use a shallow Transformer for modelconstruction, and design a normalization method Adjust QKNorm to adapt theselow-resource tasks (i.e., insufficient training data). Finally, to alleviatethe out-of-vocabulary problem, we propose a rulebased repair component toimprove the performance of automatic shellcode generation. In our empiricalstudy, we select a highquality corpus Shellcode IA32 as our empirical subject.This corpus was gathered from two real-world projects based on the line-by-linegranularity. We first compare DualSC with six state-of-the-art baselines fromthe code generation and code summarization domains in terms of four performancemeasures. The comparison results show the competitiveness of DualSC. Then, weverify the effectiveness of the component setting in DualSC. Finally, weconduct a human study to further verify the effectiveness of DualSC.

DualSC: Automatic Generation and Summarization of Shellcode via Transformer and Dual Learning