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name: adversarial-machine-learning description: "Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks."

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