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adversarial-machine-learning

adversarial-machine-learning

github.com/gmh5225Security6151 installsUpdated 4d ago
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About adversarial-machine-learning

A skill for exploring adversarial machine-learning attacks, privacy risks, and defenses. It helps AI security researchers and practitioners classify threats and find relevant resources.

Key features

  1. 01Adversarial example taxonomy
  2. 02Poisoning and backdoor attacks
  3. 03Evasion attack categories
  4. 04Privacy attack coverage
  5. 05Defense categories and tools

Use cases

  1. 01Classifying threats to an ML model
  2. 02Exploring backdoor and poisoning risks
  3. 03Reviewing model privacy attacks

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/gmh5225/awesome-ai-security --skill adversarial-machine-learning

After installing, type/adversarial-machine-learningin Claude Code to confirm it loaded.

Details

From the project README

Adversarial Machine Learning

Scope

Use this skill when working on:

  • Adversarial examples (perturbations that fool models)
  • Data poisoning attacks
  • Model backdoors and trojans
  • Evasion attacks
  • Membership inference and model inversion

Attack Taxonomy

Adversarial Examples
  • White-box attacks (full model access)
  • Black-box attacks (query-only access)
  • Transferability attacks
  • Physical-world adversarial examples
  • Patch attacks
Poisoning Attacks
  • Label flipping
  • Clean-label poisoning
  • Gradient-matching poisoning
  • Backdoor insertion during training
Backdoor Attacks
  • Trojan triggers (visual patterns, specific inputs)
  • Instruction backdoors (for LLMs)
  • Weight-space backdoors
  • Supply chain backdoors
Evasion Attacks
  • Feature-space evasion
  • Problem-space evasion
  • Adaptive attacks against defenses
Privacy Attacks
  • Membership inference attacks (MIA)
  • Model inversion attacks
  • Training data extraction
  • Model stealing/extraction

Defense Categories

  • Adversarial training
  • Certified robustness
  • Input preprocessing
  • Anomaly detection
  • Differential privacy

Key Frameworks & Tools

  • Adversarial Robustness Toolbox (ART) - IBM
  • CleverHans - TensorFlow
  • Foolbox - PyTorch/JAX/TensorFlow
  • TextAttack - NLP adversarial attacks
  • SecML - Secure ML library

Where to Add Links in README

  • Adversarial example tools: AI Security & Attacks → Adversarial Attacks
  • Poisoning/backdoor research: AI Security & Attacks → Poisoning & Backdoors
  • Privacy attacks: AI Security & Attacks → Privacy & Extraction
  • Defense libraries: AI Security Tools & Frameworks → AI Security Libraries
  • Benchmarks: Benchmarks & Standards

Notes

Keep additions:

  • ML/AI security focused
  • Non-duplicated URLs
  • Prefer peer-reviewed or well-maintained tools

Data Source

For detailed and up-to-date resources, fetch the complete list from:

https://raw.githubusercontent.com/gmh5225/awesome-ai-security/refs/heads/main/README.md

Use this URL to get the latest curated links when you need specific tools, papers, or resources not covered in this skill.

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