Evidence Alignment and Transfer Boundaries in Llm Extraction Defense And Structured Knowledge Extraction: Let Them Steal Trapping and Enhancing multi-modal Relation Extraction

Abstract

Research spanning LLM extraction defense and structured knowledge extraction increasingly joins methods that were developed for different objects and decisions. Here, a honeypot knowledge graph that redirects model-extraction queries toward low-transferability knowledge is compared with reinforcement-learning-guided graph diffusion for multimodal relation extraction to determine which claims can travel across those boundaries and which remain context dependent. The review draws on two focal records and 12 established sources already present in the project evidence cache. Its comparative framework links attack modeling, honeypot knowledge, and query economics to downstream questions of utility preservation and adaptive attackers. 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 contribution is a decision-oriented synthesis that connects method selection to failure cost and treats reproducibility, provenance, and bounded generalization as first-order design requirements.