In this paper a novel speaker verification spoofing countermeasure based on analysis of linear prediction error is presented. The method analyses the energy of the prediction error, prediction gain and temporal parameters related to the prediction error signal. The idea of the proposed algorithm and its implementation is described in detail. Various binary classifiers were researched to separate human and spoof classes. When tested on the corpora provided for the ASVspoof 2015 Challenge, the proposed countermeasure yielded much better results than the baseline spoofing detector based on local binary patterns (LBP). It is hoped that the proposed method can help in developing a generalised countermeasure able to detect spoofing attacks based on different variants of speech synthesis, voice conversion, and, potentially, also other spoofing algorithms.