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Robustness & Adversarial Testing

Identify and quantify adversarial and distribution-shift vulnerabilities in brain_ai systems.

Updated Feb 28, 2026
Search Tags:#robustness#calibration#adversarial-training#adversarial-testing#ood-detection#corruption-benchmarks
One-click install
npx skills add https://github.com/sovr610/refffiy --skill robustness-adversarial-testing
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Robustness & Adversarial Testing
Source: https://github.com/sovr610/refffiy/tree/main/brain-ai-dev/skills/robustness-adversarial
Command: npx skills add https://github.com/sovr610/refffiy --skill robustness-adversarial-testing

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, pytest, and includes scripts (resource) and references (resource) and assets (resource) components.

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.

Frequently Asked Questions about Robustness & Adversarial Testing

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

FAQPage Schema
How do I test my AI models for adversarial attacks and distribution shifts?▼

Adversarial training improves robustness by generating adversarial examples during training using techniques like PGD-AT, Free-AT, and TRADES, forcing models to learn invariant features and better withstand future adversarial attacks.

How does out-of-distribution detection work for identifying model failure modes?▼

Out-of-distribution detection identifies model failure modes by calculating energy and mahalanobis OOD scores to measure when inputs deviate from training data distributions, flagging unreliable predictions across encoders, workspace, and reasoning components.

Can I run adversarial testing workflows using PyTorch and pytest?▼

Yes, you can run adversarial testing workflows using PyTorch and pytest, which are the required dependencies for executing the robustness suite, attack generation scripts, and calibration validation templates across your AI stack.

What is the best way to balance adversarial robustness with model accuracy?▼

The best way to balance adversarial robustness with accuracy is applying curriculum adversarial training workflows, which progressively increase attack difficulty during training to strengthen resilience without severely degrading standard performance.

What are corruption benchmarks and how do they evaluate AI system resilience?▼

Corruption benchmarks evaluate AI system resilience by applying systematic data corruptions to measure performance degradation across multi-layer architectures, helping identify specific failure modes in encoders, HTM, workspace, and reasoning components.

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