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 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]