There is provided systems and methods for adversarial sample generation and adversarial malware threat prevention. The method including: receiving an input executable sample; extracting features of the input executable sample and applying feature mapping to determine components of the features; determining a binary classifier representing whether the executable sample is adversarial using one or more machine learning models, the one or more machine learning models taking the components as input, the one or more machine learning models trained using, at least, generated adversarial samples, generating the generated adversarial samples includes determining code caves in training executable samples and inserting generated payloads as benign samples at the determined code caves; and where the binary classifier indicates adversarial, dropping the input executable sample, otherwise outputting the input executable sample.