ctf-ai-ml
Provides AI and machine learning techniques for CTF challenges. Use when attacking ML models, crafting adversarial examples, performing model extraction, prompt injection, membership inference, training data poisoning, fine-tuning manipulation, neural network analysis, LoRA adapter exploitation, LLM jailbreaking, or solving AI-related puzzles.
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GitHub хранилище- Звезди на репото
- 3337
- Лиценз
- MIT
- Последна проверка на записа
- 22.09.2026 г.
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Командата отваря избора на skills от репозитория. Прочетете файла и условията на автора преди изпълнение.
npx skills add ljagiello/ctf-skillsСтраницата копира текст. Не инсталира и не изпълнява нищо на устройството ви.
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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.