Core Insights - The white paper systematically outlines five key risks threatening the security of large models, including infrastructure security risks, content security risks, data and knowledge base security risks, agent security risks, and user-end security risks [1][3] - The proposed dual governance strategy combines "external security" and "platform-native security" to create a comprehensive protection network for AI applications [1][3] Group 1: Key Risks - The first category of risks is infrastructure security risks, which include device control, supply chain vulnerabilities, denial-of-service attacks, and misuse of computing resources [1] - The second category is content security risks, involving non-compliance with core values, false or illegal content, model hallucinations, and prompt injection attacks [1] - The third category focuses on data and knowledge base security risks, highlighting issues such as data breaches, unauthorized access, privacy abuse, and intellectual property concerns [1] - The fourth category addresses agent security risks, where the increasing autonomy of agents blurs the security boundaries in areas like plugin invocation, computing resource scheduling, and data flow [1] - The fifth category is user-end security risks, which encompass permission control, API call monitoring, execution of malicious scripts, and security during MCP execution [1] Group 2: Security Solutions - The white paper emphasizes a dual governance strategy: "external security" acts as a flexible response to real-time risks, while "platform-native security" builds a robust security foundation from the ground up [1] - 360's products, including enterprise-level knowledge bases and agent construction platforms, are designed to embed security deeply within the platform, ensuring compliance with national and industry standards [2] - The three main platform products work together to address inherent security challenges, such as data leakage, uncontrolled agent behavior, and terminal misuse, thereby establishing a stable foundation for AI applications [2] - 360 has implemented these capabilities across various sectors, including government, finance, and manufacturing, transforming theoretical security into practical solutions [2] - The company aims to collaborate with academia and industry to promote security standards and technology sharing, contributing to a safer and more trustworthy AI ecosystem [2]

Core Insights - The 360 Digital Security Group released the "Large Model Security White Paper" at the World Internet Conference, outlining five key risks associated with large model operations and proposing a dual-track governance strategy for security [1][2] Group 1: Key Risks Identified - The white paper identifies five critical risks threatening large model security: 1. Infrastructure security risks, including device control, supply chain vulnerabilities, denial-of-service attacks, and misuse of computing resources 2. Content security risks, involving non-compliance with core values, false or illegal content, large model hallucinations, and prompt injection attacks 3. Data and knowledge base security risks, highlighting issues like data leakage, unauthorized access, privacy abuse, and intellectual property concerns 4. Intelligent agent security risks, where the boundaries of security become blurred due to increased autonomy in agent operations 5. User-end security risks, which encompass permission control, API call monitoring, execution of malicious scripts, and security in MCP execution [1] Group 2: Proposed Security Solutions - The white paper advocates a "plug-in security + platform-native security" dual governance strategy, which offers two main advantages: 1. High adaptability and low deployment costs, allowing for quick integration into various enterprise environments without redundant development 2. Rapid response capabilities with independent monitoring and interception mechanisms that can identify and block real-time threats, such as abnormal computing consumption or malicious content, in milliseconds [2] Group 3: Implementation and Future Plans - 360 has successfully implemented these security capabilities across various sectors, including government, finance, and manufacturing, transforming large model security from theoretical concepts into practical, actionable solutions - The company plans to collaborate with academia and industry to promote the establishment of security standards and technology sharing, aiming to build a safe and trustworthy AI ecosystem [2]

Core Viewpoint - The 360 Digital Security Group released the "Large Model Security White Paper" at the World Internet Conference, outlining five key risks associated with large model operations and proposing a dual-track governance strategy for security [1][2]. Summary by Sections Key Risks Identified - The white paper identifies five critical risks threatening large model security: 1. Infrastructure security risks, including device control, supply chain vulnerabilities, denial-of-service attacks, and misuse of computing resources 2. Content security risks, involving non-compliance with core values, false or illegal content, model hallucinations, and prompt injection attacks 3. Data and knowledge base security risks, highlighting data breaches, unauthorized access, privacy abuse, and intellectual property issues 4. Agent security risks, where the increasing autonomy of agents blurs security boundaries in areas like plugin calls, computing resource scheduling, and data flow 5. User-end security risks, covering permission control, API call monitoring, malicious script execution, and MCP execution security [1][2]. Governance Strategy - The white paper proposes a dual-track governance strategy of "external security + platform-native security": - External security acts as an "external bodyguard" to flexibly respond to real-time risks, while platform-native security serves as an "internal armor" to strengthen the foundational security [2][3]. - External security focuses on monitoring and defending against risks related to computing hosts, software ecosystems, input/output content, and model hallucinations [2]. - Platform-native security embeds security capabilities into core components, enhancing the safety of supporting components and ensuring compliance throughout the process [3][4]. Product Capabilities - The company has developed a comprehensive solution for large model security, consisting of seven core product capabilities that combine external and platform-native security: - External security capabilities do not intrude on the original architecture of large models and provide flexible, rapid dynamic protection through external tools [3]. - Key products include the Large Model Guardian computing host security system, detection system, protection system, and hallucination detection and mitigation system, which together form an external barrier against infrastructure and content risks [3][4]. Implementation and Future Plans - The platform-native security approach is reflected in three major products: an enterprise-level knowledge base, an agent construction and operation platform, and an agent client, which collectively address internal security challenges [4]. - The company has successfully implemented these capabilities across various sectors, including government, finance, and manufacturing, transforming large model security from theory into practical solutions [4][5]. - Future plans involve collaboration with academia and industry to promote security standards and technology sharing, aiming to build a safe and trustworthy AI ecosystem [5].

Core Insights - The 360 Digital Security Group released the "Large Model Security White Paper" at the World Internet Conference, outlining five key risks associated with large model operations and proposing a dual-track security strategy to enhance AI safety and reliability [1][4][12] Risk Summary - The white paper identifies five critical risks to large model security: 1. Infrastructure security risks, including device control, supply chain vulnerabilities, denial-of-service attacks, and misuse of computing resources [5] 2. Content security risks, which involve non-compliance with core values, false or illegal content, model hallucinations, and prompt injection attacks [5] 3. Data and knowledge base security risks, highlighting issues like data leakage, unauthorized access, privacy abuse, and intellectual property concerns [5] 4. Intelligent agent security risks, where the increasing autonomy of agents blurs security boundaries in areas like plugin invocation and data flow [5] 5. User-end security risks, including permission control, API call monitoring, malicious script execution, and security in multi-cloud platforms [5] Security Strategy - The white paper proposes a dual-track governance strategy of "External Security + Platform Native Security" to address the identified risks: - External security acts as an "external bodyguard" for real-time risk management, while platform native security serves as an "internal armor" to strengthen foundational safety [7][10] Implementation of Security Measures - The external security approach focuses on proactive monitoring and defense against threats to computing hosts, software ecosystems, input/output content, and model hallucinations, offering adaptability and rapid response capabilities [9] - The platform native security embeds safety features into core components, ensuring compliance with national and industry standards while providing comprehensive protection for intelligent applications [9][10] Comprehensive Defense Capabilities - The company has developed a comprehensive solution comprising seven core product capabilities that integrate external and platform native security, addressing risks from infrastructure to content layers [10] - The external security products include systems for computing host security, detection, protection, and hallucination detection, while platform native products safeguard data, control intelligent agent behavior, and secure user endpoints [10][12] Industry Application - The security capabilities have been successfully implemented across various sectors, including government, finance, and manufacturing, transforming theoretical security measures into practical solutions [12]