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.

翻译：我们第一步是处理自动生成贝壳编码的任务,即从自然语言评论开始,在利用软件脆弱性时用作有效载荷的小型代号。我们收集并发布一套新的数据集(Shellcode_IA32),由具有挑战性但共同的装配指令及其自然语言描述组成。我们试验神经机器翻译的标准方法(NMT),以确定这项任务的基线性能水平。