Robustness & Adversarial Testing

What problem does it solve?

This Skill provides a comprehensive framework to assess and strengthen the resilience of brain_ai systems against adversarial attacks, distribution shifts, and related robustness challenges.

Core Features & Use Cases

• Attack generation and evaluation (PGD-AT, TRADES, Free-AT, AutoAttack) to improve robustness while managing the trade-off with accuracy.
• OOD detection, corruption benchmarks, and calibration analysis to detect and measure failure modes across multi-layer architectures.
• End-to-end workflows and reference templates to validate security and reliability across encoders, workspace, HTM, and reasoning components.

Quick Start

Run the robustness suite to validate all aspects: adversarial testing, OOD detection, corruption benchmarks, calibration, and curriculum adversarial training workflows across the brain_ai stack.