How does Naïve Bayes behave in adversarial or noisy feature environments?

Naïve Bayes is moderately robust to noise but can be misled by adversarially correlated features.

Random noise tends to average out due to multiplicative likelihood aggregation. However, adversarial manipulation can introduce misleading correlated features that disproportionately influence posterior probabilities. Smoothing and feature selection improve robustness.