剖析开放AI模型的安全隐患，提出增强可信度的系统性方案。
Building Trust: Foundations of Security, Safety and Transparency in AI
- 梳理公开AI模型的安全风险与生命周期管理缺失问题
- 提出面向开发者与用户的综合安全增强策略
- 适合关注AI治理与可信部署的研究者与从业者
本文探讨了日益普及的公开可用AI模型所构成的生态系统及其对安全与可靠性格局的潜在影响。随着AI模型广泛应用，理解其潜在风险与漏洞至关重要。我们回顾了当前的安全与可靠性状况，指出追踪困难、修复滞后以及缺乏明确的模型生命周期与所有权流程等挑战。文章提出了全面的策略，以提升模型开发者和终端用户的安全保障能力。本研究旨在为更标准化的AI模型开发与运营中的安全、可靠性和透明度提供基础支撑，并推动相关开放生态系统的健康发展。
原文摘要 · Abstract (English)
This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their potential risks and vulnerabilities is crucial. We review the current security and safety scenarios while highlighting challenges such as tracking issues, remediation, and the apparent absence of AI model lifecycle and ownership processes. Comprehensive strategies to enhance security and safety for both model developers and end-users are proposed. This paper aims to provide some of the foundational pieces for more standardized security, safety, and transparency in the development and operation of AI models and the larger open ecosystems and communities forming around them.
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