While the CEFS metric is effective in guiding the selection of Principal Adversarial Domains (PADs) based on coverage of the entire feature space, it may have limitations in scenarios where the feature space is highly complex or non-linear. In such cases, the metric may struggle to accurately capture the true distribution of adversarial examples, leading to suboptimal selection of PADs. Additionally, the CEFS metric relies on the assumption that the selected PADs will generalize well to unseen adversarial attacks, which may not always hold true in practice. It is essential to validate the effectiveness of the metric across a wide range of scenarios and datasets to ensure robust performance.