大模型网络安全风险识别与评价体系研究
电子技术应用
蔡超雄
广州宸祺出行科技有限公司
摘要： 为全面识别多模态大语言模型的网络安全风险，构建安全评价体系并提出应对策略，以提升其安全性与可靠性，首先分析风险类别，通过多模态数据融合构建风险识别模型，并运用层次分析法和模糊综合评价法构建安全评价体系；再经实验验证模型与体系的有效性、稳定性。研究结果表明，风险识别模型能够准确识别风险，安全评价体系能够合理评估安全状况。本研究为多模态大语言模型的安全风险识别提供创新性思路，所构建的模型与体系可显著增强风险识别能力，为多模态大语言模型的安全研究提供了理论支撑，推动大模型安全发展。
中圖分類號：TP393.08 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.256839
中文引用格式： 蔡超雄. 大模型網絡安全風險識別與評價體系研究[J]. 電子技術應用，2026，52(2)：45-51.
英文引用格式： Cai Chaoxiong. Research on the identification and evaluation system of network security risks in large language models[J]. Application of Electronic Technique，2026，52(2)：45-51.
中文引用格式： 蔡超雄. 大模型網絡安全風險識別與評價體系研究[J]. 電子技術應用，2026，52(2)：45-51.
英文引用格式： Cai Chaoxiong. Research on the identification and evaluation system of network security risks in large language models[J]. Application of Electronic Technique，2026，52(2)：45-51.
Research on the identification and evaluation system of network security risks in large language models
Cai Chaoxiong
Guangzhou Chenqi Travel Technology Co.， Ltd.
Abstract： This study aims to comprehensively identify the network security risks of multimodal large language models, construct a security evaluation system, and propose response strategies to enhance their security and reliability. The research method first analyzes the risk categories, constructs a risk identification model through multimodal data fusion, and uses the Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation Method to construct a security evaluation system. Then, the effectiveness and stability of the model and system are verified through experiments. The research results indicate that the risk identification model can accurately identify risks, and the security evaluation system can reasonably assess the security situation. This study provides innovative ideas for the security risk identification of multimodal large language models. The constructed model and system can significantly enhance the risk identification ability, providing theoretical support for the security research of multimodal large language models and promoting the safe development of large language models.
Key words : multimodal large language model；network security； risk identification；analytic hierarchy process；fuzzy comprehensive evaluation method；security evaluation system；response strategy
引言
多模態大語言模型作為人工智能領域重大突破，整合文本、圖像、音頻等多種模態數據，具備強大的語言理解和生成能力，在智能問答、內容創作、計算機視覺等領域得到廣泛應用[1]。同時，多模態大語言模型也帶來諸多網絡安全風險。因此，對多模態大語言模型的網絡安全風險進行識別，并制定有效的應對策，具有重要意義。
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作者信息：
蔡超雄
（廣州宸祺出行科技有限公司，廣東 廣州 510500）
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