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Corner case generation is crucial for the safety evaluation of highway automated vehicles (HAVs).  Existing studies have not considered the systematic generation of corner cases for interactive driving scenarios like highway ramp merging or roundabouts entering. We propose an ambiguity-guided adversarial behavior planning algorithm to generate confusing behavior for the primary other road user (PORU) in interactive scenarios. The PORU is modeled as a cost-minimizing agent with hierarchical intentions. The adversarial PORU plans actions to confuse the HAVs by maximizing the ambiguity with respect to its intentions, while also taking nominal behavior planning goals into consideration. Two interactive scenarios are studied: highway merging and pedestrian crossing. A corner case testing scheme is designed and implemented for both scenarios to evaluate the performance of different HAVs comprehensively and objectively.