Privacy and Application Challenges in LLMs
Three new research papers on arXiv address different aspects of large language model implementation and security.
Ensemble Privacy Defense for Knowledge-Intensive LLMs
According to arXiv:2512.03100v1, researchers have investigated privacy vulnerabilities in Retrieval-Augmented Generation (RAG) and Supervised Finetuning (SFT) systems. The paper examines membership inference attacks against these “predominant paradigms for equipping Large Language Models (LLMs) with external knowledge for diverse, knowledge-intensive tasks,” as stated in the abstract.
Econometric Framework for LLM Analysis
ArXiv:2412.07031v3 presents an econometric framework for researchers using LLMs to analyze text. According to the paper, “Large language models (LLMs) enable researchers to analyze text at unprecedented scale and minimal cost,” allowing them to “revisit old questions and tackle novel ones with rich data.”
KV-Cache Privacy Risks
A third paper (arXiv:2508.09442v2) examines privacy vulnerabilities in the Key-Value (KV) cache mechanism used to accelerate LLM inference. The research focuses on the KV cache, which “stores intermediate attention computations (Key and Value pairs) to avoid redundant calculations” and is described as “a fundamental mechanism for accelerating Large Language Model (LLM) inference.”