  Writing exploits for security assessment is a challenging task. The writer
needs to master programming and obfuscation techniques to develop a successful
exploit. To make the task easier, we propose an approach (EVIL) to
automatically generate exploits in assembly/Python language from descriptions
in natural language. The approach leverages Neural Machine Translation (NMT)
techniques and a dataset that we developed for this work. We present an
extensive experimental study to evaluate the feasibility of EVIL, using both
automatic and manual analysis, and both at generating individual statements and
entire exploits. The generated code achieved high accuracy in terms of
syntactic and semantic correctness.
