Papers › Shellcode_IA32: A Dataset for Automatic Shellcode Generation
Shellcode_IA32: A Dataset for Automatic Shellcode Generation
Pietro Liguori, Erfan Al-Hossami, Domenico Cotroneo, Roberto Natella, Bojan Cukic, Samira Shaikh
We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode_IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.
Code
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Tasks
Datasets
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Code Generation | Shellcode_IA32 | LSTM-based Sequence to Sequence | BLEU-4 | 62.97 | #3 of 3 | Archive leaderboard | report |
| Code Generation | Shellcode_IA32 | LSTM-based Sequence to Sequence | Exact Match Accuracy | 51.55 | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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