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We study submodular optimization in adversarial context, applicable to machine learning problems such as feature selection using data susceptible to uncertainties and attacks. We focus on Stackelberg games between an attacker (or interdictor) and a defender where the attacker aims to minimize the defender’s objective of maximizing a k-submodular function. We allow uncertainties arising from the success of attacks and inherent data noise, and address challenges due to incomplete knowledge of the prob- ability distribution of random parameters. Specifically, we introduce Distributionally Risk-Averse k-Submodular Interdiction Problem (DRA \(k\)-SIP) and Distributionally Risk-Receptive \(k\)-Submodular Interdiction Problem (DRR \(k\)-SIP) along with finitely convergent exact algorithms for solving them. The DRA \(k\)-SIP solution allows risk-averse interdictor to develop robust strategies for real-world uncertainties. Conversely, DRR \(k\)-SIP solution suggests aggressive tactics for attackers, willing to embrace (distributional) risk to inflict maximum damage, identifying critical vulnerable components, which can be used for the defender’s defensive strategies. The optimal values derived from both DRA \(k\)-SIP and DRR \(k\)-SIP offer a confidence interval-like range for the expected value of the defender’s objective function, capturing distributional ambiguity. We conduct computational experiments using instances of feature selection and sensor placement problems, and Wisconsin breast cancer data and synthetic data, respectively.