ctf-ai-ml

CTF AI/ML Attack Techniques

When to Use

• Challenge involves ML model files (.pt, .pth, .safetensors, .onnx, .h5)
• Target is an AI chatbot, LLM-based application, or ML classifier
• Need to craft adversarial examples to fool image/text classifiers
• Challenge provides model weights for analysis or manipulation
• AI/ML platform security testing (model extraction, membership inference)

Quick Start

pip install torch transformers numpy scipy Pillow safetensors scikit-learn
file model.*
python3 -c "import torch; m=torch.load('model.pt'); print(type(m), m.keys() if hasattr(m,'keys') else '')"

Decision Tree

1. Model weight file (.pt/.safetensors) → model-attacks.md
   • Weight perturbation negation, model inversion, LoRA merging, encoder collision
2. Image classifier to fool → adversarial-ml.md
   • FGSM, PGD, C&W attacks, adversarial patches, evasion, data poisoning
3. LLM/chatbot target → llm-attacks.md
   • Prompt injection, jailbreaking, token smuggling, tool use exploitation
4. Pure math/crypto inside ML → Switch to ctf-crypto
5. Compiled model binary → Switch to ctf-reverse
6. Python jail wrapped in chatbot → Switch to ctf-misc

Pivot Signals

• If challenge is pure cryptography/number theory with no ML → /ctf-crypto
• If reverse engineering compiled inference binary → /ctf-reverse
• If Python sandbox escape inside chatbot wrapper → /ctf-misc
• If web app with AI features (prompt injection via web) → /ai-security/prompt-injection