面向安全治理的大语言模型风险分析与应对策略研究OA

To address the challenges of fragmented understanding of Large Language Model(LLM)security risks and the inadequacy of LLM risk classification and grading frameworks,this study aims to construct a comprehensive framework that integrates risk mechanism analysis,quantitative assessment,and governance practices.Theoretically,this study synthesizes and reconstructs multiple foundational theories,including socio-technical systems,social systems theory,and safety science,to reveal that risks originate from a dual trigger mechanism of the model's"internal complexity"and"external interaction."It consequently dissects risks into two primary dimensions—"internal safety"and"application security"—providing a unified theoretical foundation for a systematic governance framework.Methodologically,the study introduces"Risk Label Cards"as a standardized tool and employs an"Artificial Intelligence+Human Expert Collaboration"approach to structurally analyze real-world security incidents.Combined with an improved DREAD(damage,reproducibility,exploitability,affected users,discoverability)risk matrix model,it establishes a complete assessment methodology that spans from qualitative identification to quantitative grading.The research culminates in the construction of a systematic risk classification system and a three-tiered(high,medium,low)risk landscape covering major risk types.The"dual-dimensional driven"risk analysis and governance framework constructed in this study provides a systematic theoretical tool for the precise assessment and governance of LLM risks,effectively bridging the"theory-practice gap"in governance.Furthermore,with its theoretical compatibility and dynamic characteristics,the framework provides a reference for continuously tracking and understanding the evolution of LLM security risks and for security policy research.