Shellcode_IA32: A Dataset for Automatic Shellcode Generation

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Code Generation Shellcode_IA32 LSTM-based Sequence to Sequence BLEU-4 62.97 # 3
Exact Match Accuracy 51.55 # 3