Core Insights - The application of AI large models has raised significant data security concerns, particularly regarding the phenomenon of "prompt leakage" [1][2] - The complexity of AI systems and their interactions increases the risk of sensitive information being inadvertently exposed during data transmission and processing [1][2] Group 1: AI Large Models and Data Security - AI large models are akin to vast databases containing sensitive information, which can be exposed through unintentional prompts during interactions [1] - Unlike traditional databases, AI models possess reasoning capabilities that make them more susceptible to "prompt leakage" [1] - The risk of "prompt leakage" necessitates a comprehensive examination of the entire interaction system to identify potential security vulnerabilities [1] Group 2: Risks and Mitigation Strategies - The risks associated with "prompt leakage" include personal privacy breaches, loss of corporate competitive advantage, and potential threats to national security [2] - Effective measures must be implemented to prevent "prompt leakage," including monitoring interaction processes and analyzing response content [2] - The emergence of new technologies like Model Context Protocol (MCP) introduces additional complexities and security risks, highlighting the critical need for data security in AI applications [2]