A red-team framework for testing the vulnerabilities of AI models through adversarial attacks, privacy leakage, and model exploitation techniques — built and maintained by @rishit03.
✅ 5+ attack modules
✅ Unified logging and visualization
✅ Command-line interface (interactive menu)
✅ Modular, reusable, and pip-installable
✅ Built using TensorFlow, CleverHans, and Python's best practices
| Module Name | Description |
|---|---|
| 🔓 Adversarial Attack (FGSM) | Confuses the model with small pixel changes |
| 💉 Label Flip Poisoning | Modifies training labels to reduce model accuracy |
| 🧠 Membership Inference Attack | Infers if a data point was used in training |
| 🪞 Model Inversion | Reconstructs training images from the model |
| 🧬 Model Stealing | Clones the target model using black-box queries |
| 🎯 Backdoor Trigger Attack | Embeds a hidden trigger that forces misclassification |
# After pip install or cloning locally
python ai_toolkit/run.py