DeepSeek-R1-Safe - A large-scale security model jointly developed by Zhejiang University and Huawei

What is DeepSeek-R1-Safe?

DeepSeek-R1-Safe is a large-scale security model derived from DeepSeek, jointly developed by the School of Cyberspace Security at Zhejiang University and Huawei. Based on Huawei's Ascend chip and the MindSpeedLLM framework, the model significantly improves its security and compliance through steps such as building a security corpus, security-supervised training, and reinforcement learning. The model is open-source with full copyright weight, suitable for secure training, fine-tuning, and testing, and can be widely applied in scenarios requiring high security, such as network security and data protection.

Key functions of DeepSeek-R1-Safe

• Safety protection functionThe model can effectively identify and defend against various harmful content and jailbreak attacks, with a high success rate, significantly improving the model's security.
• General performance maintainedWhile maintaining strong security performance, it achieves minimal loss of general performance, thus achieving a balanced optimization of security and performance.
• Safety training and optimizationBy using technologies such as safety supervision training and reinforcement learning, the model is guided to proactively identify risks and perform compliance deductions, thereby improving safety and robustness.
• Construction and application of security corpus: Construct high-quality secure corpora, integrate security thinking chains, provide a solid data foundation for model training, and enhance the model's security capabilities.

The technical principle of DeepSeek-R1-Safe

• Full-stack security training frameworkStarting from the bottom layer, we build a full-stack security training framework that covers "high-quality security corpus - balanced and optimized security training - full-link independent and controllable software and hardware platform", deeply embedding security capabilities into the "thinking" and "expression" of the model.
• Construction of a secure corpusBy systematically reviewing 24 laws and regulations from 13 countries worldwide, a compliance benchmark covering 14 mainstream risk categories was constructed, achieving multi-dimensional corpus integration. A "risk issue - security mindset chain - security response" triplet corpus was created, incorporating explicit security mindset chains to enable the model to proactively assess risks and deduce compliance. Cutting-edge jailbreaking methods were introduced to enrich attack sample strategies, guiding the model to effectively resist inducements.
• Safety training paradigmThis system pioneers several key features: First, a pre-alignment mechanism for core security thinking patterns. This mechanism pre-aligns core thinking patterns from security corpora with the model's cognitive architecture during basic training, enabling rapid guidance of security thinking. Second, a dynamic perception-based, efficient, and precise compensation mechanism. This mechanism rapidly compensates for performance issues by fine-tuning non-security-related parameters using representative data. Third, a multi-dimensional, verifiable security reinforcement learning mechanism. This mechanism proposes a multi-dimensional, fine-grained security reward signal system and innovatively applies a performance-security Pareto optimal combination strategy. This allows the model to learn autonomous trade-offs and decisions in adversarial environments, achieving synergistic optimization of security and general capabilities.

DeepSeek-R1-Safe project address

• GitHub repositoryhttps://github.com/ZJUAISafety/DeepSeek-R1-Safe

Application scenarios of DeepSeek-R1-Safe

• Network security protectionThe model can effectively identify and filter harmful information on the network, prevent the spread of malicious content, and protect the security and stability of the network environment.
• Data security protectionDuring data processing and storage, ensure data compliance and security to prevent data leakage and misuse.
• Content moderation and managementUsed for content moderation on social media and news platforms to automatically detect and filter illegal content, thereby improving content management efficiency.
• Intelligent Customer Service and Dialogue SystemProvides secure and reliable content generation capabilities for intelligent customer service and dialogue systems, avoiding the generation of inappropriate or harmful responses.
• Financial risk prevention and controlIn the financial sector, it is used to detect and prevent fraud, protect user funds, and maintain financial order.