Knowledge Graphs (KGs) provide structured and interpretable representations of real-world entities and relations. While dynamic KGs attempt to capture real-time changes, they typically treat updates as independent facts. This overlooks a critical challenge: a factual, localized update can contradict and invalidate previously correct knowledge, requiring revisions beyond the localized update to maintain KG consistency. Many of these inconsistencies arise from events whose effects propagate through relational dependencies, necessitating coordinated multi-hop reasoning rather than isolated changes. To address this, we introduce a model-agnostic framework for cascading KG update identification that leverages conformal prediction to provide reliable uncertainty guarantees over the cascade as a whole, accounting for dependencies among multi-hop update candidates. Building on this foundation, we further develop a graph-based KG update scoring framework that integrates large language models (LLMs) to enrich event representations with world knowledge. Experiments on two newly constructed real-world datasets, designed to reflect scenarios where events necessitate coordinated multi-hop updates, demonstrate that our framework establishes a strong baseline while offering calibrated confidence estimates, providing an effective solution for event-driven KG consistency restoration.

Retrieval-Augmented Generation (RAG) and Supervised Finetuning (SFT) have become the predominant paradigms for equipping Large Language Models (LLMs) with external knowledge for diverse, knowledge-intensive tasks. However, while such knowledge injection improves performance, it also exposes new attack surfaces. Membership Inference Attacks (MIAs), which aim to determine whether a given data sample was included in a model’s training set, pose serious threats to privacy and trust in sensitive domains. To this end, we first systematically evaluate the vulnerability of RAG- and SFT-based LLMs to various MIAs. Then, to address the privacy risk, we further introduce a novel, model-agnostic defense framework, Ensemble Privacy Defense (EPD), which aggregates and evaluates the outputs of a knowledge-injected LLM, a base LLM, and a dedicated judge model to enhance resistance against MIAs. Comprehensive experiments show that, on average, EPD reduces MIA success by up to 27.8% for SFT and 526.3% for RAG compared to inference-time baseline, while maintaining answer quality.