In recent years, dynamic user verification has become one of the basic pillars for insider threat detection. From these threats, the research presented in this paper focuses on masquerader attacks, a category of insiders characterized by being intentionally conducted by persons outside the organization that somehow were able to impersonate legitimate users. Consequently, it is assumed that masqueraders are unaware of the protected environment within the targeted organization, so it is expected that they move in a more erratic manner than legitimate users along the compromised systems. This feature makes them susceptible to being discovered by dynamic user verification methods based on user profiling and anomaly-based intrusion detection. However, these approaches are susceptible to evasion through the imitation of the normal legitimate usage of the protected system (mimicry), which is being widely exploited by intruders. In order to contribute to their understanding, as well as anticipating their evolution, the conducted research focuses on the study of mimicry from the standpoint of an uncharted terrain: the masquerade detection based on analyzing locality traits. With this purpose, the problem is widely stated, and a pair of novel obfuscation methods are introduced: locality-based mimicry by action pruning and locality-based mimicry by noise generation. Their modus operandi, effectiveness, and impact are evaluated by a collection of well-known classifiers typically implemented for masquerade detection. The simplicity and effectiveness demonstrated suggest that they entail attack vectors that should be taken into consideration for the proper hardening of real organizations.