AI/ML CTF

Goal: solve AI and machine-learning CTF tasks with artifact-first triage, controlled experiments, and reproducible evidence.

When this skill applies

• model files, checkpoints, embeddings, LoRA adapters, classifiers, serialized pipelines, feature extractors, or model APIs
• adversarial examples, model inversion, extraction, poisoning, membership inference, or prompt-injection tasks
• LLM tool-use, RAG, context, or guardrail bypass puzzles in authorized challenge environments

Operating model

1. Classify the dominant lane: model artifact, training data, feature pipeline, inference API, LLM app, RAG/tool flow, or embedded model.
2. Define the oracle: target class, recovered secret, membership decision, extracted weights, prompt leak, tool action, or checker response.
3. Load the closest methodology before selecting tools.
4. Load only the reference matching the lane; do not run every attack family by habit.
5. Build a controlled local reproduction or differential query harness before claiming a model weakness.
6. Record parameters, seeds, prompts, inputs, outputs, confidence, and exact validation proof.