Evidence Alignment and Transfer Boundaries in Llm Extraction Defense And Physics-Editable World Models

Abstract

This review examines a shared methodological problem in LLM extraction defense and physics-editable world models: how evidence from a honeypot knowledge graph that redirects model-extraction queries toward low-transferability knowledge can be placed in analytical dialogue with a large-scale dataset organized around physically editable world-model factors without erasing differences in scale, assumptions, or intended use. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: attack modeling, honeypot knowledge, query economics, utility preservation, adaptive attackers. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. Comparison reveals recurring trade-offs among attack modeling, honeypot knowledge, and query economics. These trade-offs do not support a universal ranking; instead, they identify the operating envelope within which each method remains credible and the perturbations most likely to expose fragile conclusions. The resulting framework supports reproducible comparison while preserving differences between study designs, and it identifies concrete points at which transfer claims should be narrowed or retested.