ctf-ai-ml

1# CTF AI/ML
2 
3Quick reference for AI/ML CTF challenges. Each technique has a one-liner here; see supporting files for full details.
4 
5## Prerequisites
6 
7**Python packages (all platforms):**
8```bash
9pip install torch transformers numpy scipy Pillow safetensors scikit-learn
10```
11 
12**Linux (apt):**
13```bash
14apt install python3-dev
15```
16 
17**macOS (Homebrew):**
18```bash
19brew install python@3
20```
21 
22## Additional Resources
23 
24- [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
25- [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
26- [llm-attacks.md](llm-attacks.md) - Prompt injection (direct/indirect), LLM jailbreaking, token smuggling, context window manipulation, tool use exploitation
27 
28---
29 
30## When to Pivot
31 
32- If the challenge becomes pure math, lattice reduction, or number theory with no ML component, switch to `/ctf-crypto`.
33- If the task is reverse engineering a compiled ML model binary (ONNX loader, TensorRT engine, custom inference binary), switch to `/ctf-reverse`.
34- 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`.
35 
36## Quick Start Commands
37 
38```bash
39…