Gait recognition is a relatively new biometric and as a result relatively little effort has yet been devoted to studying spoofing attacks against it. This chapter examines the effects of two different spoofing attacks against two different state-of-the-art gait recognition systems. The first attack uses clothing impersonation where an attacker replicates the clothing of a legitimately enrolled individual. The second attack is a targeted attack where an imposter deliberately selects the legitimately enrolled subject whose gait signature is closest to the attacker. The analysis presented here reveals that both systems are vulnerable to both attacks. In particular, if both attacks are combined and the systems have acceptance thresholds set at the EER of their baseline performance, the attacks cause the FAR to rise from 5% to between 60 and 95%. The chapter describes two countermeasures that can be applied to minimise the effects of the spoofing attacks. Using the same acceptance thresholds the countermeasure to clothing attacks reduces the FAR performance under clothing impersonation from 40 to 15%. Likewise, the targeting countermeasure reduces the FAR for targeted attacks from 20 to 2.5% sufficient to even improve on the baseline performance.