{
  "id": 14873,
  "url": "https://arxiv.org/abs/2607.28617",
  "title": "AISPA: User-Centric System Prompt Auditing for Large Language Model Applications",
  "summary": "arXiv:2607.28617v1 Announce Type: cross Abstract: 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 s",
  "authors": "Xiangning Lin, Shenzhe Zhu, Shu Yang, Zhenyu Zhang, Haoqian Zhang, Yipeng Zhao, Chengxuan Qian, Tianwei Wang, Ziheng Zhang, Zhenlong Yuan, Dingcheng Wang, Juncheng Wu, Yuan Si, Jiaxin Liu, Baolong Bi, Robert Mahari, Tobin South, Dazza Greenwood, Zexue He, Rishi Bommasani, Sophia Kazinnik, Andreas Haupt, Samuele Marro, Erik Brynjolfsson, Alex Pentland, Jiaxin Pei",
  "category": "research",
  "topics": "regulation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-31T04:00:00.000Z",
  "fetched_at": "2026-07-31T05:10:57.675Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/14873",
  "original_url": "https://arxiv.org/abs/2607.28617",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}