White-box monitoring is increasingly adopted as an auditing tool as Large Language Models (LLMs) are deployed in daily operations to ensure safe model behavior. However, white-box monitors can be circumvented, and the mechanisms underlying such evasion have not been systematically characterized, nor have principled defenses been proposed. This work addresses both challenges. Controlled red-team experiments reveal two primary evasion strategies: geometric shifting, defined as the systematic migration of information between linear and non-linear representational subspaces, and covariance manipulation. These mechanisms account for the failure of single-detector approaches, as information migrates to subspaces inaccessible to individual detectors. This issue is urgent due to growing evidence that models are becoming evaluation-aware, enabling those with misaligned objectives to exploit these vulnerabilities and evade monitoring during deployment. In response, \textsc{SafetyNet} is introduced as a principled ensemble, with dual purpose: it provides further empirical validation that our mechanistic findings are real and actionable, and it offers a concrete starting point for future work on robust latent-space monitoring. The study experiment across five model families on the MAD and Anthropic Sleeper Agent benchmark, with SafetyNet achieving around 100\% AUROC scores outscoring Beatrix and CROW. The code is available at: https://github.com/MaheepChaudhary/eval-aware-evasion

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