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Status: Draft — Incubating OWASP Project
This standard is under active development. Contributions and feedback are very welcome.
When you download a pre-trained model from HuggingFace or any other repository, you're essentially trusting a black box. You don't know what data it was trained on, whether that data contained personal information, how it's been tested for adversarial attacks, or what its known failure modes are. For most hobby projects that's fine. For anything touching real users, regulated industries, or sensitive data — it really isn't.
Model cards have existed for a while, and they're a good idea. But the existing formats focus almost entirely on performance metrics and fairness considerations. Security is largely an afterthought, if it's there at all.
This project is trying to fix that. We're building an open standard that defines what security-relevant information a model card should contain — things like training data provenance, PII exposure risk, known vulnerabilities, supply chain details, and regulatory compliance status. Something that a security team can actually use when assessing whether a model is safe to deploy.
A few things have converged at once:
A security-focused model card standard gives developers, auditors, and procurement teams a common language and a checklist they can actually act on.
Here's a flavour of what a security-focused model card looks like under this standard:
model_card_version: "0.1"
model_name: "example-text-classifier"
model_version: "1.2.0"
training_data:
sources:
- name: "Common Crawl (filtered)"
pii_assessment: "Presidio scan applied; residual risk: low"
license: "CC0"
pii_removal_method: "Microsoft Presidio + manual review on 5% sample"
data_cutoff_date: "2024-01-01"
security:
known_vulnerabilities: []
adversarial_testing: "Red-teamed against prompt injection; results in /security/redteam-report.md"
supply_chain:
base_model: "bert-base-uncased"
base_model_source: "https://huggingface.co/bert-base-uncased"
compliance:
eu_ai_act_risk_tier: "Limited"
gdpr_considerations: "No personal data retained post-training"
This is a simplified excerpt. The full schema covers additional fields for incident history, output filtering, deployment constraints, and more.
/
├── README.md ← you are here
├── standard/
│ └── OWASP-Modelcard-Standard-Proposal.md ← the specification
├── templates/
│ └── model-card-template.md ← blank template to copy and fill in
├── examples/
│ └── llm-example.md ← worked example for a large language model
├── schema/
│ └── model-card-schema.json ← machine-readable JSON schema
├── mappings/
│ └── owasp-nist-euaiact-mapping.md ← how this standard relates to other frameworks
├── CONTRIBUTING.md ← how to get involved
├── CHANGELOG.md
└── LICENSE ← CC BY 4.0
This standard is designed to complement, not replace, existing work:
This is an early-stage project and there's plenty of room to shape where it goes. A few ways to contribute:
#ai-ml-securityIf you're not sure where to start, the open issues labelled good first issue are a reasonable place to look.
This project is currently going through the OWASP new project process. Once accepted it will move to the OWASP GitHub organisation at github.com/OWASP/owasp-model-card-security-standard.
This standard is published under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to use, adapt, and redistribute it — including commercially — as long as you give appropriate credit.
owasp/main
| Framework | Relationship |
|---|
| OWASP Top 10 for LLM Applications | This standard helps document mitigations for several LLM Top 10 risks |
| OWASP AI Exchange | Aligned with AI security guidance; cross-referenced where relevant |
| NIST AI Risk Management Framework | Standard fields map to AI RMF govern, map, measure, and manage functions |
| EU AI Act | Covers transparency and documentation obligations for limited and high-risk AI systems |
| HuggingFace Model Cards | Extends the HuggingFace format with security-specific fields |
| Google Model Cards | Compatible with Google's approach; adds security layer on top |