As artificial intelligence (AI) becomes deeply embedded in critical services and everyday products, it is increasingly exposed to security threats which traditional cyber defenses were not designed to handle. In this paper, we investigate how cyber threat intelligence (CTI) may evolve to address attacks that target AI systems. We first analyze the assumptions and workflows of conventional threat intelligence with the needs of AI-focused defense, highlighting AI-specific assets and vulnerabilities. We then review and organize the current landscape of AI security knowledge. Based on this, we outline what an AI-oriented threat intelligence knowledge base should contain, describing concrete indicators of compromise (IoC) for different AI supply-chain phases and artifacts, and showing how such a knowledge base could support security tools. Finally, we discuss techniques for measuring similarity between collected indicators and newly observed AI artifacts. The review reveals gaps and quality issues in existing resources and identifies potential future research directions toward a practical threat intelligence framework tailored to AI.

翻译：随着人工智能（AI）深度融入关键服务和日常产品，其日益暴露于传统网络防御机制未曾设计应对的安全威胁之下。本文探讨了网络威胁情报（CTI）应如何演进以应对针对AI系统的攻击。我们首先分析了传统威胁情报的假设和工作流程与聚焦AI的防御需求之间的差异，重点阐述了AI特有的资产与脆弱性。随后，我们梳理并归纳了当前AI安全知识的现状。在此基础上，我们勾勒出面向AI的威胁情报知识库应包含的内容，描述了针对不同AI供应链阶段及产物的具体入侵指标（IoC），并展示了此类知识库如何支持安全工具。最后，我们讨论了度量已收集指标与新观测AI产物间相似性的技术方法。本综述揭示了现有资源中的空白与质量问题，并指出了未来构建实用化AI定制威胁情报框架的潜在研究方向。