Test scoring models vary in their generality, some even adjust for examinees answering multiple-choice items correctly by accident (guessing), but no models, that we are aware of, automatically adjust an examinee's score when there is internal evidence of cheating. In this study, we use a combination of jackknife technology with an adaptive robust estimator to reduce the bias in examinee scores due to contamination through events such as having access to some of the test items in advance of the test administration. We illustrate our methodology with a data set of test items we knew to have been divulged to a subset of the examinees.