adversarial-machine-learning
Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/gmh5225/awesome-ai-security --skill adversarial-machine-learning- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads adversarial-machine-learning automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
FAQ
What does the adversarial-machine-learning skill do?
Guide for adversarial machine learning: adversarial examples, data poisoning, model backdoors, and evasion attacks.
How do I install adversarial-machine-learning?
Run npx skills add https://github.com/gmh5225/awesome-ai-security --skill adversarial-machine-learning in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/adversarial-machine-learning/ (personal) or .claude/skills/adversarial-machine-learning/ (project).
Which AI agents can use adversarial-machine-learning?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is adversarial-machine-learning safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains adversarial-machine-learning?
It is published by gmh5225 in gmh5225/awesome-ai-security. It has 51 recorded installs.
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