Adversarial Examples have been shown to be applicable in data augmentation domains on many occasions. However, they are rarely studied in the context of High Energy Physics, as well as in the context of tabular data. To combat this, we have conceptualized a novel adversarial attack algorithm - called the Random Distribution Shuffle Attack - which aims to generate adversaries fooling deep neural networks, while minimizing the perturbations to the individual features' one-dimensional Distributions.