大模型安全研究报告2024

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - The global competition in foundation model technology, spurred by ChatGPT, is driving a significant shift from narrow AI to general AI, indicating a transformative change in human-computer interaction and application development [4] - The commercialization of foundation models is accelerating, but it also introduces new security risks such as model "hallucinations," instruction injection attacks, and the democratization of cyberattacks [4] - The report emphasizes the need for a comprehensive security framework for foundation models, focusing on both inherent security and the security enabled by these models [4][28] Summary by Sections 1. Foundation Model Evolution - The evolution of foundation models has gone through three phases: exploration (2017-2021), explosion (2022-2023), and enhancement (2024-present) [17][19][20] - The exploration phase saw the introduction of pre-trained language models like GPT-3, which marked a shift towards large-scale pre-trained deep neural networks [17][18] - The explosion phase was characterized by the release of various language models, leading to a competitive landscape [19] - The enhancement phase is witnessing the rise of multimodal models capable of processing diverse types of data, improving understanding and interaction with the physical world [20] 2. Security Challenges Faced by Foundation Models - Foundation models face significant security challenges due to their integration into various sectors, which can lead to unintended security impacts [21] - The report categorizes security risks into four components: training data, algorithm models, system platforms, and business applications [21] - Key risks include data leakage, model robustness issues, and the potential for malicious use of models [21][22][23][24][25] 3. New Security Opportunities Presented by Foundation Models - Foundation models offer new solutions to existing cybersecurity challenges, enhancing threat detection and response capabilities [27] - They can improve the accuracy and timeliness of threat identification and response through advanced data analysis and automated processes [27] - The models' self-learning capabilities can enhance data security technologies, making them more accessible and effective [27] 4. Scope of Foundation Model Security Research - The security of foundation models encompasses both their inherent security and the security they can provide to other systems [28] - The report outlines a security framework that includes security goals, attributes, protection targets, and measures [28][29] - The framework aims to ensure the reliability, compliance, and robustness of foundation models while protecting systems, data, users, and behaviors [29]