Security Risks and Countermeasures for Large Language Models in Language Education: A Study Based on the DREAD Framework

Abstract

This study systematically analyzes the security risks associated with the application of large language models (LLMs) in language education based on the DREAD threat assessment framework. It points out that, as “digital native speakers,” LLMs are deeply integrated into the entire teaching process, introducing novel educational risks such as “language hallucinations,” cultural bias, and prompt injection. These risks manifest specifically as high acquisition costs in knowledge internalization, high classroom reproducibility of risks, low exploitation thresholds, broad impact scope, and low visibility. In response, this study constructs a multi-layered dynamic governance system, proposing to reduce acquisition costs through a combination of technical filtering and manual verification, manage reproducibility and exploitation thresholds by implementing tiered access controls and full-process monitoring, and strengthen the digital literacy of both teachers and students to control the risk impact scope and enhance visibility. The research indicates that only by establishing a collaborative ecosystem led by educational principles, empowered by technology, supported by institutions, and founded on literacy can LLMs truly evolve into constructive tools that promote language proficiency development and cross-cultural understanding.