Auditing the Auditor: Limitations, Attack Surfaces, and Stewardship Requirements for Applying v43 in AI Governance

The v43 kernel has been stress-tested across high-pressure socio-political domains, demonstrating its capacity to constrain expression, surface collapse signals, and resist rhetorical escalation. However, publishing stress tests alone risks creating a credibility imbalance: capability is demonstrated while limitation remains implicit. This paper provides a formal audit of v43’s limitations when applied to AI governance, treating the kernel as a diagnostic and stance-constraining system rather than a decision-making or safety mechanism. We identify concrete attack surfaces and structural blind spots, including evidential asymmetry, model-specific retrieval effects, prompt-shape steering, non-deterministic variance exploitation, and performative compliance. We argue that v43 cannot guarantee epistemic completeness, factual correctness, or value resolution, and that any attempt to deploy it as an autonomous governance mechanism would violate its own architectural commitments. We conclude by formalising a stewardship-bound deployment model in which v43 functions as a diagnostic and orienting constraint under explicit human accountability. This audit is not external critique but internal necessity: under the Architecture of Limitation, a system that cannot name its own boundaries is structurally dishonest.