A challenging problem with today's increasingly large and distributed software systems is their performance behavior. To help developers avoid or detect mistakes that lead to performance problems, many researchers in software performance engineering have come up with classifications of such problems, called antipatterns. To test the approaches for antipattern detection, data from running systems is required. However, the usefulness of this data is doubtful as it may or may not include manifestations of performance problems. In this paper, we classify existing performance antipatterns w.r.t. their suitability for being injected and, based on this, introduce an extensible tool that allows to inject instances of these antipatterns into existing applications. The approach can be useful for researchers to test and validate their automated runtime problem evaluation and prevention techniques. Using two exemplary performance antipatterns, it is demonstrated that the injection is easily possible and produces feasible, though currently rather clinical results.