Recently, the emergence of naturalistic adversarial patch (NAP), which possesses a deceptive appearance and various representations, underscores the necessity of developing robust detection strategies.However, existing approaches fail to differentiate the deep-seated natures in adversarial patches, i.e., aggressiveness and naturalness, leading to unsatisfactory precision and generalization against NAPs.To tackle this issue, we propose NAPGuard to provide strong detection capability against NAPs via the elaborated critical feature modulation framework.For improving precision, we propose the aggressive feature aligned learning to enhance the model's capability in capturing accurate aggressive patterns. Considering the challenge of inaccurate model learning caused by deceptive appearance, we align the aggressive features by the proposed pattern alignment loss during training. Since the model could learn more accurate aggressive patterns, it is able to detect deceptive patches more precisely.To enhance generalization, we design the natural feature suppressed inference to universally mitigate the disturbance from different NAPs. Since various representations arise in diverse disturbing forms to hinder generalization, we suppress the natural features in a unified approach via the feature shield module. Therefore, the models could recognize NAPs within less disturbance and activate the generalized detection ability.Extensive experiments show that our method surpasses state-of-the-art methods by large margins in detecting NAPs (improve 60.24% AP\@0.5 on average).