model-inversion

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.