Work

A Comprehensive Security Evaluation Framework for Chinese Large Language Models

Year: 2025

Type: conference-paper

Abstract: This paper presents a pioneering security evaluation framework for Chinese generative large language models (LLMs), addressing a significant gap in the field. The framework is supported by a comprehensive dataset of over 12,000 samples, systematically organized across four key dimensions: value evaluation, robustness, training data security, and prompt injection attacks. Each dimension is further subdivided into detailed subcategories.

DOI: 10.1145/3701716.3717528

Authors: Yuli Yang, Zhenhua Huang, Zhaohong Jia, Junfeng Fang

Institutions: Anhui University, University of Science and Technology of China, National University of Singapore

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Funders: National Natural Science Foundation of China, Universitas Brawijaya

Awards: 面向客户需求的智能服务单元动态集成优化理论与方法研究, G6195042517

OpenAlex ID: W4410636372

FWCI:

Cites: 3

Cited by:

Related to: 10