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):** ```bash pip install torch transformers numpy scipy Pillow safetensors scikit-learn ``` **Linux (apt):** ```bash apt install python3-dev ``` **macOS (Homebrew):** ```bash brew install python@3 ``` ## Additional Resources - [model-attacks.md](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-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](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 ```bash # Inspect model file f...