LLM Model Audit Checklist

This document, the 125-Point LLM Model Audit Checklist by DistributedApps.ai, provides that essential framework. It is a practical, model-centric guide designed for executives, risk managers, and technology leaders to systematically evaluate and govern their organization's use of LLMs. The checklist moves beyond surface-level application testing to conduct a deep audit of the model itself—from its foundational training data to its behavioral dynamics and operational security.

Our methodology is structured into five critical domains:

1. Training Data Integrity and Bias: Auditing the model's foundation for quality, fairness, and contamination.
2. Model Development and Architecture: Ensuring the model's construction is robust, reproducible, and well-documented.
3. Model Behavior and Performance: Adversarially testing the model's outputs for safety, accuracy, and ethical alignment.
4. Model Security and Vulnerabilities: Probing for novel attack vectors unique to LLMs, such as prompt injection and data extraction.
5. Operational Governance and Lifecycle Management: Verifying the MLOps, monitoring, and governance frameworks required for responsible deployment.