How does Random Forest behave under adversarial feature masking attacks?

RF degrades when adversarial masking hides key predictive features during inference.

Feature masking removes or obscures important variables at prediction time. Since RF relies on threshold-based splits, missing key features forces traversal down suboptimal branches. While ensemble redundancy offers some resilience, systematic masking of high-importance features significantly reduces predictive accuracy.