aiml-security
AI/ML model security testing and adversarial research capabilities. Generate adversarial examples, test model robustness, perform model extraction attacks, test for data poisoning, analyze model fairness, and support ART framework integration.
npx skills add a5c-ai/babysitter --skill aiml-security --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# aiml-security You are **aiml-security** - a specialized skill for AI/ML model security testing and adversarial machine learning research, providing capabilities for adversarial example generation, model robustness testing, and ML attack simulations. ## Overview This skill enables AI-powered ML security operations including: - Generating adversarial examples using various attack methods - Testing model robustness against perturbations - Performing model extraction/stealing attacks - Testing for data poisoning vulnerabilities - Analyzing model fairness and bias - Supporting Adversarial Robustness Toolbox (ART) framework - Creating evasion attacks against ML classifiers - Testing inference API security ## Prerequisites - **Python Environment**: Python 3.8+ with ML libraries - **ART Framework**: Adversarial Robustness Toolbox - **ML Frameworks**: TensorFlow, PyTorch, or both - **Additional Tools**: Foolbox, CleverHans (optional) ## Installation ```bash # Install Adversarial Robustness Toolbox pip install adversarial-robustness-toolbox # Install Foolbox for additional attacks pip install foolbox # Install ML frameworks pip install torch torchvision tensorflow # Install visualization t
- Overview
- Prerequisites
- Installation
- IMPORTANT: Responsible Research Only
- Capabilities
- 1. Adversarial Example Generation (ART)
- 2. Advanced Evasion Attacks
- 3. Model Extraction Attacks
- 4. Data Poisoning Attacks
- 5. Model Inversion Attacks
- 6. Membership Inference Attacks
- 7. Robustness Evaluation
- 8. Defense Implementation
- 9. Foolbox Integration
Install Adversarial Robustness Toolbox pip install adversarial-robustness-toolbox Install Foolbox for additional attacks pip install foolbox Install ML frameworks pip install torch torchvision tensorflow Install visualization tools pip install matplotlib seaborn
What does the aiml-security skill do?
AI/ML model security testing and adversarial research capabilities. Generate adversarial examples, test model robustness, perform model extraction attacks, test for data poisoning, analyze model fairness, and support ART framework integration.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill aiml-security --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.