AISPA: User-Centric System Prompt Auditing for Large Language Model Applications
Researchers introduced AISPA, a framework to audit hidden system prompts in LLMs. It evaluates prompts across eight dimensions to improve transparency and user trust.
- AISPA is a new framework for auditing hidden system prompts in LLMs.
- It evaluates prompts across eight user-centric dimensions.
- The tool aims to close the trust gap in commercial AI deployment.
- System prompts are rarely disclosed despite governing model behavior.
System prompts act as the hidden instructions governing foundation models, yet they remain largely undisclosed in commercial products. This lack of transparency creates significant challenges for accountability and trust in AI systems.
The paper introduces Artificial Intelligence System Prompt Assurance, or AISPA, as a solution to this opacity. It provides a structured approach for users to systematically examine and audit these critical configuration instructions.
AISPA evaluates system prompts based on eight specific dimensions that are relevant to end users. By applying this framework, stakeholders can better understand how AI behaviors are shaped and ensure they align with safety and ethical standards.
Provides a method to verify how system instructions impact model outputs and safety.
Offers a framework to audit AI vendors and ensure internal compliance with governance standards.
Highlights the growing need for infrastructure that supports AI safety and transparency.
Introduces a concrete framework for studying AI governance and the role of system instructions.
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