CTF AI/ML

CTF AI/ML

Quick reference for AI/ML CTF challenges. Each technique has a one-liner here; see supporting files for full details.

Prerequisites

Python packages (all platforms):

pip install torch transformers numpy scipy Pillow safetensors scikit-learn

Linux (apt):

apt install python3-dev

macOS (Homebrew):

brew install python@3

Additional Resources

• model-attacks.md - Model weight perturbation negation, model inversion via gradient descent, neural network encoder collision, LoRA adapter weight merging, model extraction via query API, membership inference attack
• adversarial-ml.md - Adversarial example generation (FGSM, PGD, C&W), adversarial patch generation, evasion attacks on ML classifiers, data poisoning, backdoor detection in neural networks
• llm-attacks.md - Prompt injection (direct/indirect), LLM jailbreaking, token smuggling, context window manipulation, tool use exploitation

When to Pivot

• If the challenge becomes pure math, lattice reduction, or number theory with no ML component, switch to /ctf-crypto.
• If the task is reverse engineering a compiled ML model binary (ONNX loader, TensorRT engine, custom inference binary), switch to /ctf-reverse.
• If the challenge is a game or puzzle that merely uses ML as a wrapper (e.g., Python jail inside a chatbot), switch to /ctf-misc.

Quick Start Commands

# Inspect model file format
file model.*