  Anti-malware engines are the first line of defense against malicious
software. While widely used, feature engineering-based anti-malware engines are
vulnerable to unseen (zero-day) attacks. Recently, deep learning-based static
anti-malware detectors have achieved success in identifying unseen attacks
without requiring feature engineering and dynamic analysis. However, these
detectors are susceptible to malware variants with slight perturbations, known
as adversarial examples. Generating effective adversarial examples is useful to
reveal the vulnerabilities of such systems. Current methods for launching such
attacks require accessing either the specifications of the targeted
anti-malware model, the confidence score of the anti-malware response, or
dynamic malware analysis, which are either unrealistic or expensive. We propose
MalRNN, a novel deep learning-based approach to automatically generate evasive
malware variants without any of these restrictions. Our approach features an
adversarial example generation process, which learns a language model via a
generative sequence-to-sequence recurrent neural network to augment malware
binaries. MalRNN effectively evades three recent deep learning-based malware
detectors and outperforms current benchmark methods. Findings from applying our
MalRNN on a real dataset with eight malware categories are discussed.
