Computerized adaptive testing is subject to security problems, as the item bank content remains operative over long periods and administration time is flexible for examinees. Spreading the content of a part of the item bank could lead to an overestimation of the examinees' trait level. The most common way of reducing this risk is to impose a maximum exposure rate (r[superscript max]) that no item should exceed. Several methods have been proposed with this aim. All of these methods establish a single value of (r[superscript max]) throughout the test. This study presents a new, the multiple-(r[superscript max]) method, that defines as many values of r[superscript max] the number of items presented in the test. In this way, it is possible to impose a high degree of randomness in item selection at the beginning of the test, leaving the administration of items with the best psychometric properties to the moment when the trait level estimation is most accurate. The implementation of the multiple-r[superscript max] method is described and is tested in simulated item banks and in an operative bank. Compared with a single maximum exposure method, the new method has a more balanced usage of the item bank and delays the possible distortion of trait estimation due to security problems, with either no or only slight decrements of measurement accuracy. (Contains 1 table and 13 figures.)