skills.rest
maruakshay
maruakshayCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

adversarial-robustness

Assess adversarial robustness of AI systems against evasion attacks.

4|Updated Apr 27, 2026
Search Tags:#model-evaluation#ml-security#adversarial-robustness#robustness-evaluation#adversarial-attack#input-normalization
One-click install
npx skills add https://github.com/maruakshay/mii-ai-security --skill adversarial-robustness
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: adversarial-robustness
Source: https://github.com/maruakshay/mii-ai-security/tree/main/skills/adversarial-robustness
Command: npx skills add https://github.com/maruakshay/mii-ai-security --skill adversarial-robustness

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Adversarial robustness gaps reveal how human-intended meaning can be misinterpreted by models under tiny input perturbations, threatening safety and reliability.

Core Features & Use Cases

  • Evaluate resilience of safety classifiers and content filters against evasion attacks.
  • Analyze transferability of adversarial examples across model versions and configurations.
  • Use cases include validating guardrails in production, conducting red-team assessments, and strengthening model evaluation pipelines.

Quick Start

Run an adversarial-robustness assessment against your deployed model to identify vulnerabilities and validate defenses.

Frequently Asked Questions about adversarial-robustness

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

FAQPage Schema
How do I evaluate ML model robustness against adversarial evasion attacks?▼

To evaluate adversarial robustness, you must run a structured assessment pipeline that applies input normalization and adversarial test sets to measure the perturbation resilience of your safety classifiers.

What is adversarial robustness evaluation in machine learning security?▼

Adversarial robustness evaluation assesses how tiny input perturbations can cause models to misinterpret human-intended meaning, revealing vulnerabilities in safety classifiers and content filters.

How do I test if my content filters are vulnerable to adversarial inputs?▼

You test content filters by deploying an adversarial test set to probe for evasion attacks, validating your production guardrails through defense-in-depth checks across multiple classifiers.

Can I analyze adversarial example transferability across different model versions?▼

Yes, you can analyze adversarial example transferability by running robustness evaluations across model versions and configurations to identify shared evasion vulnerabilities.

Does evaluating perturbation resilience require input normalization?▼

Yes, input normalization is a required component of the structured evaluation pipeline to accurately measure perturbation resilience and assess defense-in-depth capabilities.

What is the best way to conduct a red-team assessment for ML security?▼

The best way to conduct ML security red-team assessments is to execute an adversarial robustness evaluation pipeline that systematically tests safety classifiers against evasion attacks.

Related Skills

Explore More Software Engineering Skills→
rampstackcorampstackco
523

security-baseline

Establish a security baseline for websites and web apps.

Official
Advanced
haJ1thaJ1t

purple-team-analyzer

Simulate attack paths and validate defense controls in a codebase.

Community
Advanced
PuckAPIPuckAPI
2

data-pipeline

Automate sports analytics pipelines for data collection, predictions, and drift detection.

Official
Intermediate
HopeArtOrgHopeArtOrg
8

protection-algorithm

Apply SPSA-PGD adversarial perturbation to protect images from AI training.

Official
Advanced