AgentSkillsCN

adversarial-machine-learning

对抗性机器学习指南：对抗样本、数据中毒、模型后门与规避攻击。

SKILL.md
--- frontmatter
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

Data Source

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

code
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.