Magika

Detailed Introduction

Magika is an open-source tool from Google Security Research that leverages a compact deep-learning model to identify file content types with millisecond latency on a single CPU. Trained on roughly 100M samples across 200+ content types, Magika aims to provide high-accuracy classification for large-scale systems (e.g., Gmail, Drive, and security pipelines), enabling correct routing of files to downstream scanners and processors.

Main Features

• Small and fast: the model is only a few megabytes and achieves millisecond inference per file after loading, suitable for high-concurrency batch processing.
• Multi-language bindings: provides a Rust CLI, Python API, JavaScript/TypeScript bindings (used for the web demo), and an in-progress Go binding.
• High coverage and reliability: supports 200+ content types and a per-type threshold mechanism with configurable modes such as high-confidence, medium-confidence, and best-guess.
• Easy to try: installable via pipx, pip, NPM, or try the browser-based demo without installation.

Use Cases

• Security & content inspection: route uploaded or transferred files to proper scanners and policy engines.
• Large-scale offline processing: fast pre-classification and distribution for logs, mail archives, and storage systems.
• Automation pipelines: integrate with CI/CD, sampling services, or forensic tooling for file-type extraction and analytics.

Technical Features

Magika uses a tailored lightweight deep-learning model and a per-type confidence thresholding strategy to balance precision (~99% on test sets) with low latency and resource footprint. Techniques such as limited input sampling and optimized batch inference make performance nearly independent of file size, enabling scalable CPU-based deployments.