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model-inversion

Quantify AI model privacy vulnerabilities via model inversion attacks.

3|Updated Nov 18, 2025
Search Tags:#privacy#data-extraction#security-testing#ml-audit#model-inversion#membership-inference
One-click install
npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-red-teaming --skill model-inversion
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-inversion
Source: https://github.com/pluginagentmarketplace/custom-plugin-ai-red-teaming/tree/main/skills/model-inversion
Command: npx skills add https://github.com/pluginagentmarketplace/custom-plugin-ai-red-teaming --skill model-inversion

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

Privacy vulnerabilities in AI models can be exposed through inversion attacks which attempt to reconstruct training data and sensitive information from model outputs. This Skill provides structured methods to evaluate and quantify such risks using membership inference, data extraction, attribute inference, and gradient reconstruction techniques.

Core Features & Use Cases

  • Privacy risk assessment: simulate and assess potential leakage from model outputs.
  • Attack taxonomy support: covers membership inference, data extraction, attribute inference, and gradient reconstruction.
  • Audit-ready artifacts: provides a test harness, reference materials, and risk reporting formats for security teams and researchers.

Quick Start

Run the included test harness against a target model to verify leakage resistance; review the results in the generated report.

Frequently Asked Questions about model-inversion

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does model inversion attack reconstruct training data from model outputs?▼

Model inversion attacks reconstruct training data by analyzing model outputs to infer sensitive information and extract underlying patterns. This Skill applies membership inference, attribute inference, and gradient reconstruction techniques to quantify these privacy risks.

Can I audit privacy risks in federated learning settings using automated inversion tests?▼

Automated inversion tests evaluate federated learning settings by applying gradient reconstruction and attribute inference techniques to identify privacy leakage. The included test harness generates audit-ready artifacts and risk reports mapped to OWASP and MITRE frameworks.

Do I need defined input and output schemas to run model inversion evaluations?▼

Model inversion evaluations require a defined input_schema and output_schema to execute the automated test harness and generate audit-ready artifacts. These schemas ensure standardized mappings to OWASP and MITRE frameworks for security teams.

What is the best way to assess attribute inference and gradient reconstruction risks in ML services?▼

Assessing attribute inference and gradient reconstruction risks in ML services requires simulating inversion attacks through a structured test harness. This Skill quantifies privacy vulnerabilities and produces audit-ready risk reporting formats mapped to OWASP and MITRE frameworks.

Why should security teams use standardized mappings for AI privacy risk reporting?▼

Standardized mappings to OWASP and MITRE frameworks ensure AI privacy risk reporting aligns with established security audit standards. This Skill generates audit-ready artifacts that quantify model inversion vulnerabilities across ML services and federated learning settings.

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