language:

• en
  license: cc-by-4.0
  tags:
• ai-governance
• ai-security
• threat-modeling
• ai-risk
• vulnerability-taxonomy
• prompt-injection
• data-poisoning
• adversarial-ml
• agentic-ai
• nist-ai-rmf
• iso-42001
• eu-ai-act
• mitre-atlas
• owasp-llm
• stride-ai
• red-teaming
• responsible-ai
• grc
• model-governance
• quantitative-risk
  task_categories:
• text-classification
• tabular-classification
• question-answering
  size_categories:
• n<1K
  pretty_name: AI Threat Taxonomy and Vulnerability Registry
  configs:
• config_name: threats
  data_files:
  • split: train
    path: data/train.csv
• config_name: vulnerabilities
  data_files:
  • split: train
    path: data/vulnerabilities.csv
• config_name: stride
  data_files:
  • split: train
    path: data/stride_mapping.csv
• config_name: priority_matrix
  data_files:
  • split: train
    path: data/priority_matrix.csv

dataset_info:
description: >
A comprehensive, structured taxonomy of AI threat vectors and AI
vulnerabilities mapped to ISO/IEC 42001 control themes, NIST AI RMF
functions, MITRE ATLAS attack patterns, OWASP LLM Top 10, and STRIDE-AI
categories. Covers generative AI, agentic AI, predictive AI, and
cross-cutting supply chain threats. Designed for AI red teaming,
governance risk assessments, model risk management, and regulatory
compliance use cases. Created by Prof. Hernan Huwyler, MBA CPA CIAO,
Academic Director at IE Law School Center for Risk and Compliance and
Senior Manager at Capgemini Applied AI Lab.
citation: >
@misc{huwyler2025aithreat,
author = {Huwyler, Hernan},
title = {AI Threat Taxonomy and Vulnerability Registry},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/hwyler/ai-threat-taxonomy-and-vulnerabilities}
}
features:
- name: threat_id
dtype: string
- name: threat_name
dtype: string
- name: threat_category
dtype: string
- name: ai_system_type
dtype: string
- name: stride_category
dtype: string
- name: nist_ai_rmf_function
dtype: string
- name: iso_42001_control_theme
dtype: string
- name: mitre_atlas_technique
dtype: string
- name: owasp_llm_reference
dtype: string
- name: description
dtype: string
- name: attack_path
dtype: string
- name: assessment_guidance
dtype: string
- name: priority_generative_ai
dtype: string
- name: priority_agentic_ai
dtype: string
- name: priority_predictive_ai
dtype: string
- name: priority_supply_chain
dtype: string
- name: related_vulnerabilities
dtype: string
- name: controls
dtype: string

AI Threat Taxonomy and Vulnerability Registry

Dataset Description

This dataset provides a practitioner-grade, machine-readable taxonomy of AI
threat vectors and AI vulnerabilities for use in red teaming, risk assessment,
model governance, and regulatory compliance programs.

The taxonomy distinguishes precisely between:

• Threat vectors: paths or mechanisms an attacker, insider, or negligent
  actor uses to exploit an AI environment
• Vulnerabilities: weaknesses in design, control, architecture, process,
  or implementation that threat vectors exploit

Coverage

• 50+ AI threat vectors organized by attack type and AI system category
• 55+ AI vulnerabilities organized by control domain
• STRIDE-AI mapping across all six threat categories
• Priority matrices for generative AI, agentic AI, predictive AI,
  and supply chain threats
• Assessment guidance for practitioners conducting red team and risk reviews
• Framework cross-references: ISO/IEC 42001, NIST AI RMF, MITRE ATLAS,
  OWASP LLM Top 10, EU AI Act

Intended Uses

• AI red team scoping and threat modeling
• AI risk register construction
• Regulatory gap assessment (EU AI Act, ISO 42001, NIST AI RMF)
• Model governance and third-party AI vendor assessment
• Executive education in AI governance and compliance

Author

Prof. Hernan Huwyler, MBA CPA CIAO
Academic Director, IE Law School Center for Risk and Compliance
Senior AI Risk and Governance Manager, Capgemini Applied AI Lab
Two-time recipient, IE Lifelong Learning Academic Excellence Award
(Management Program, 2023 and 2024)

License

Creative Commons Attribution 4.0 International (CC BY 4.0)