  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.
