AISPA:为LLM应用打造的用户中心系统提示审计框架

AISPA: User-Centric System Prompt Auditing for Large Language Model Applications

精选理由

论文用AISPA框架审计了88款商业AI产品,发现约四成产品里有损害用户利益的指令,做AI应用的朋友可以看看。

AI 摘要

AISPA是一个面向用户的系统提示审计框架,从八个维度逐条检查系统提示。研究团队用该框架审计了88款商业AI产品中的3249条指令,发现各产品设计差异悬殊:有的产品平均包含超过60条保护性指令,有的不足5条。尽管98.9%的产品至少有一条保护性指令,但只有24%覆盖全部八个维度;约40%的产品存在至少一条损害用户利益的指令,且保护性与问题指令经常共存。

原文 · arXiv cs.AI

AISPA: User-Centric System Prompt Auditing for Large Language Model Applications

System prompts are instructions configured by developers to govern the behaviors of foundation models in AI applications. They are used throughout commercial AI products, but are rarely disclosed to the public or regulators, creating a serious trust and accountability gap in the wide deployment of AI systems. In this paper, we introduce Artificial Intelligence System Prompt Assurance (AISPA), a user-centric framework for systematically auditing system prompts in AI systems. AISPA examines specific parts of a system prompt and evaluates them along eight dimensions that matter to users. We then use this framework to review 3,249 instructions from system prompts in 88 commercial AI products, classifying each instruction as either protective (of users) or problematic. Our audit surfaces four core findings. First, system prompt design varies substantially across products and developers, with some organizations averaging over 60 protective instructions per product while others average fewer than 5. Second, protective instructions are widely adopted but shallow in scope: 98.9% of products contain at least one, yet only 24% cover all eight dimensions of the AISPA taxonomy. Third, system prompts have grown steadily longer and more protective of users, suggesting that user protection is becoming a more visible concern in commercial prompt design. Fourth, despite this progress, problematic instructions remain pervasive: roughly 40% of products contain at least one instruction that works against user interests, and protective and problematic instructions frequently coexist within the same prompt. Our findings highlight the need for greater transparency, standardization, and independent oversight for system prompts in commercial AI products.