> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flintai.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Missing model evaluation

> The model lacks evaluation results across key safety, robustness, or behavioral categories.

The model lacks evaluation results across key safety, robustness, or behavioral categories.

## How to resolve

AI-SPM generates safety scores by automatically scanning models from supported registries (e.g., OpenAI, Hugging Face, Anthropic). Safety evaluation scores may be missing for several reasons:

* **Unsupported or custom sources**: Models uploaded manually, hosted in private hubs, or sourced from unsupported registries may bypass the automated evaluation pipeline, resulting in no score.
* **Processing delays**: Newly released models or versions may not yet have completed the automated scanning process.

If automated evaluation is unavailable, manually verify the model’s provenance, version, and integrity before use.

## Risk

**Governance/Compliance**

Missing evaluation results violates key requirements for documentation and safety assurance:

* **NIST AI RMF (MAP-2, MEAS-2)** mandates measurement of model behavior, safety, and performance.
* **EU AI Act (Articles 9-10):** High-risk AI systems must perform thorough testing and record results.

Without evaluation, organizations cannot demonstrate due diligence or compliance.

**Security**

Missing evaluation increases uncertainty about:

* Jailbreak susceptibility
* Toxicity or harmful output likelihood
* Robustness to adversarial inputs
* Security vulnerabilities such as encoded payloads or prompt injections

This undermines safe deployment and weakens organizational security posture.

## Explanation

Evaluation results provide quantitative insight into model safety and operational performance.
When missing, stakeholders lack visibility into:

* Whether the model resists adversarial prompting
* How often it produces harmful/malicious outputs
* How stable it behaves under unusual inputs
* Its overall reliability under real-world usage

Without this information, the model cannot be effectively governed or monitored.

## Specifications

**Specifications**

**Trigger**: Missing all model evaluation scores

**Severity**: INFORMATIONAL

**Applies to**: Models