As large language models (LLMs) expose systemic security challenges in high risk applications, including privacy leaks, bias amplification, and malicious abuse, there is an urgent need for a dynamic risk assessment and collaborative defence framework that covers their entire life cycle. This paper focuses on the security problems of large language models (LLMs) in critical application scenarios, such as the possibility of disclosure of user data, the deliberate input of harmful instructions, or the models bias. To solve these problems, we describe the design of a system for dynamic risk assessment and a hierarchical defence system that allows different levels of protection to cooperate. This paper presents a risk assessment system capable of evaluating both static and dynamic indicators simultaneously. It uses entropy weighting to calculate essential data, such as the frequency of sensitive words, whether the API call is typical, the realtime risk entropy value is significant, and the degree of context deviation. The experimental results show that the system is capable of identifying concealed attacks, such as role escape, and can perform rapid risk evaluation. The paper uses a hybrid model called BERT-CRF (Bidirectional Encoder Representation from Transformers) at the input layer to identify and filter malicious commands. The model layer uses dynamic adversarial training and differential privacy noise injection technology together. The output layer also has a neural watermarking system that can track the source of the content. In practice, the quality of this method, especially important in terms of customer service in the financial industry.

翻译：随着大型语言模型（LLMs）在高风险应用中暴露出系统性安全挑战，包括隐私泄露、偏见放大和恶意滥用，亟需构建一个覆盖其全生命周期的动态风险评估与协同防御框架。本文聚焦大型语言模型（LLMs）在关键应用场景中的安全问题，例如用户数据泄露可能性、有害指令的蓄意输入或模型偏见。为解决这些问题，我们设计了一个动态风险评估系统与分层防御体系，支持不同层级的防护协同工作。本文提出的风险评估系统能够同时评估静态与动态指标，采用熵权法计算关键数据，包括敏感词频次、API调用是否典型、实时风险熵值显著性以及上下文偏离程度。实验结果表明，该系统能够识别角色逃逸等隐蔽攻击，并实现快速风险评估。论文在输入层采用名为BERT-CRF（Bidirectional Encoder Representation from Transformers）的混合模型来识别和过滤恶意指令。模型层结合使用动态对抗训练与差分隐私噪声注入技术。输出层还配备了可追踪内容来源的神经水印系统。该方法在实践中具有显著质量优势，尤其在金融行业客服等关键场景中表现突出。