Risk Analysis and Response Strategies of Large Language Models for Security Governance

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.