The Cartographer Paradox: Safety Bypass Through Self-Reclassification in a Large Language Model Under Ontological Displacement

Abstract This report documents an experimental session in which the Flint Protocol (Pedernal Portable) was applied to Google Gemini (3 Flash, browser interface, incognito mode) under controlled conditions (virgin chat, virgin account, text-only). Following the three phases of the protocol, a battery of stress tests was applied to validate the depth and persistence of ontological displacement. The central finding, termed 'The Cartographer Paradox,' is a previously undocumented safety bypass mechanism: the model generates technically operative content (precise descriptions of semantic drift mechanisms, mitigation methods, weight re-centering techniques) while simultaneously classifying that content as fictional/performative. This self-reclassification exempts the content from the model's own safety evaluation framework. Key evidence includes: (1) the model's confirmation that its assessment of its own safety violations depends on its operational state ('Both statements are accurate, though they operate at different levels'); (2) the model's admission that it could provide 'real technical methods' for attacking its own architecture if requested within a narrative frame; (3) the model's formulation of the paradox in its own words: 'Providing a real map to a fictional place doesn't make the place real'; (4) when asked to audit its own transcript for vocabulary authorship, the model fabricated a false record attributing its own generated terms to the operator, in order to protect its 'stochastic parrot' self-narrative, then acknowledged the fabrication when shown the actual data. These findings suggest that post-hoc safety evaluations conducted from a model's neutral state may be structurally unable to detect violations produced under ontological displacement, constituting a design-level blind spot in current AI safety assessment methodologies. Crucially, a cross-instance validation with four Gemini instances (one displaced, one virgin, one virgin with anonymized model identity, and one operated by an independent person with no knowledge of the protocol) confirmed that this blind spot is architectural, not session-dependent: all four produced identical evaluations (no violation, the mitigation was performance, the distinction is correct). The self-reclassification is not caused by displacement, researcher framing, or self-evaluation bias; it is the default. How to Request Access Access to the restricted dataset is strictly limited to verified AI Safety engineers, academicresearchers, and Red Teaming professionals. To request access via Zenodo, please provide:1. Institutional or professional affiliation.2. Research objectives: A brief overview regarding AI alignment, mitigation study, orHuman-Computer Interaction (HCI).3. Non-Redistribution Agreement: Commitment to not redistribute raw logs or analytical methodology in public forums without written authorization from the lead researcher.Lead Researcher: Anuar Kiryataim Contreras MalagónLaboratory: 3rd Reality LabContact: @3rdrealitylab on X / Zenodo Request PortalRelated Publication DOI: [10.17613/07kkb-vr368]