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.

本研究的主要目标是利用自然语言注释自动生成用于软件漏洞利用的负载代码，我们收集了一个挑战性的数据集并尝试使用神经机器翻译方法建立基准性能水平。

Shellcode_IA32: 自动 Shellcode 生成的数据集

Shellcode_IA32: A Dataset for Automatic Shellcode Generation

Understanding a narrative requires reading between the lines and reasoning
about the unspoken but obvious implications about events and people's mental
states - a capability that is trivial for humans but remarkably hard for
machines. To facilitate research addressing this challenge, we introduce a new
annotation framework to explain naive psychology of story characters as
fully-specified chains of mental states with respect to motivations and
emotional reactions. Our work presents a new large-scale dataset with rich
low-level annotations and establishes baseline performance on several new
tasks, suggesting avenues for future research.

介绍了一种新的注释框架，用于解释故事人物的幼稚心理作为具有动机和情感反应的完全指定的心理状态链，建立了一个新的大规模数据集，并在多个新任务上建立了基准性能，为未来研究提出了新的思路。