ctf-ai-ml

Quick reference for AI/ML CTF challenges. Each technique has a one-liner here; see supporting files for full details. Python packages (all platforms): Linux (apt): macOS (Homebrew): - 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 - 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 . - Weight perturbation negation: Fine-tuned model suppresses behavior; recover by computing 2*W_orig - W_chal to negate the fine-tuning delta. See model-attacks.md . - LoRA adapter merging: Merge LoRA adapter W_base + alpha * (B @ A) and inspect activations or generate...