Beyond Black-Box Obfuscation: Mechanistic Analysis and Defense of White-Box Monitors

Abstract: 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: this https URL

Submission history

Access Paper:

Current browse context:

References & Citations

BibTeX formatted citation

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .