  Signature-based malware detectors have proven to be insufficient as even a
small change in malignant executable code can bypass these signature-based
detectors. Many machine learning-based models have been proposed to efficiently
detect a wide variety of malware. Many of these models are found to be
susceptible to adversarial attacks - attacks that work by generating
intentionally designed inputs that can force these models to misclassify. Our
work aims to explore vulnerabilities in the current state of the art malware
detectors to adversarial attacks. We train a Transformers-based malware
detector, carry out adversarial attacks resulting in a misclassification rate
of 23.9% and propose defenses that reduce this misclassification rate to half.
An implementation of our work can be found at
https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.
