Attacks on Recommender Systems (RS) tend to bias predictions and corrupt datasets, which may cause user distrust in the recommendations and dissatisfaction with the RS. Attacks on RSs are mounted by malicious users to "push" or promote an item, "nuke" or disparage an item, or simply to disrupt the recommendations; typically, attacks are motivated by financial gains, by a desire to "game" the system, or both. Although attack research indicates that item-based recommenders are resistant to a wide variety of push and nuke attacks, in previous work we have shown that push attacks on item-based recommenders can be effective using a multiple-target approach. In this paper, we explore nuke attacks on item-based recommenders using a multiple-target approach and variations on the Pearson Correlation calculation. We show that nuke attacks using a multiple-target approach can be configured to be effective against item-based recommenders. To evaluate the effectiveness of these attacks, we use new and existing robustness metrics and an experimental design that includes a variety of attack models, attack sizes, target item types, number of target items, and datasets.