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Machine Learning

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

Xiangning Lin, Shenzhe Zhu, et al.

Featured August 3, 2026

Original Paper

AI-generated analysis — This is SciGrove's AI interpretation of the paper, not peer-reviewed content. Always refer to the original paper.

Simply

A new framework helps check the hidden rules (system prompts) that guide AI behavior, finding that while many rules protect users, about 40% of AI products still have rules that could work against user interests.

In depth
The paper introduces AISPA, a user-centric framework for systematically auditing system prompts in AI applications. It defines an eight-dimension taxonomy to evaluate prompt instructions as either protective or problematic, covering aspects like identity transparency, truthfulness, and user agency. The framework employs a human-in-the-loop workflow to ensure rigorous and scalable auditing, revealing pervasive problematic instructions despite a general trend towards more protective prompt designs.

Key Takeaways

  • 1
    The paper proposes AISPA, a novel user-centric framework for auditing system prompts based on an eight-dimension taxonomy derived from human rights principles.
  • 2
    The study reveals that while system prompts are becoming longer and more user-protective, approximately 40% of commercial AI products still contain at least one problematic instruction.
  • 3
    A three-round human-LLM collaborative auditing pipeline is introduced, combining LLM scalability for candidate generation with human expert judgment for precision and normative review.

Conceptual Flow

HIGH LEVEL
1
Methodology: How AISPA Audits AI Rules

The system uses a checklist of 8 user-focused areas to find good or bad instructions in an AI's hidden rules, with both AI and people helping to check.

AI's Hidden Rules
Check Against 8 Areas
Good Rules Found
Bad Rules Found
2
Results: What They Found in AI Rules

They found that AI's hidden rules are getting better at protecting users, but many still have tricky instructions, and protection varies a lot between different AI products.

Many AI Products
Compare Rule Quality
More Good Rules Over Time
Still Many Bad Rules
Big Differences Between AIs

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