Traditional cybersecurity methodologies target deterministic systems and fail to address the probabilistic nature of AI, leaving systems vulnerable to attack vectors such as model inversion, data poisoning, and prompt injection. Recent industry reports indicate that a majority of organizations deploying AI lack a dedicated security strategy, with adversarial attacks increasing rapidly year-over-year. We present \textit{STRIDE-AI}, a framework that bridges the gap between high-level risk standards (NIST AI RMF) and technical vulnerability taxonomies (OWASP LLM Top 10). The framework defines a six-phase assessment lifecycle, introduces a threat modeling adaptation of classical STRIDE for AI systems, and is operationalized through a purpose-built web tool. We provide an initial validation of the approach through a black-box assessment of a deployed LLM chatbot, which successfully reduced the attack success rate from 80\% to 15\% in our sandbox case study.

翻译：传统网络安全方法面向确定性系统，无法应对人工智能的概率性本质，导致系统易受模型反转、数据投毒和提示注入等攻击向量的威胁。近期行业报告指出，多数部署AI的组织缺乏专门的安全策略，对抗性攻击逐年快速增长。我们提出STRIDE-AI框架，该框架弥合了高层级风险标准（NIST AI RMF）与技术性漏洞分类体系（OWASP LLM Top 10）之间的鸿沟。框架定义了六阶段评估生命周期，针对AI系统引入了经典STRIDE方法的威胁建模适配，并通过专用网络工具实现可操作化。我们通过部署型LLM聊天机器人的黑盒评估对方法进行了初步验证，在沙箱案例研究中成功将攻击成功率从80%降至15%。