Magika — Google AI File Type Detection Tool

What it is

Magika is a file type detection tool developed by Google that uses deep learning to identify over 200 file types with 99%+ accuracy. Unlike traditional tools that rely on magic bytes or file extensions, Magika analyzes file content using a trained model to make accurate determinations.

It serves security engineers who need reliable file classification for malware scanning, content processing pipelines that handle heterogeneous uploads, and developers building systems where file type matters for routing and validation.

How it saves time or tokens

Magika replaces fragile extension-based checks and unreliable magic-byte heuristics with a single inference call. This eliminates the need for cascading fallback logic when traditional detection fails. The model runs locally without API calls, keeping latency low and avoiding per-request costs. With an estimated 3,200 tokens for the workflow configuration, integration is straightforward.

How to use

1. Install Magika via pip or use the pre-built CLI binary.
2. Point it at a file or directory to get file type predictions with confidence scores.
3. Integrate the Python API into your pipeline for programmatic file routing.

Example

# Install and run Magika CLI
pip install magika
magika /path/to/unknown_file
# Output: /path/to/unknown_file: Python source (python)

# Batch scan a directory
magika /uploads/*

from magika import Magika

m = Magika()
result = m.identify_path('/path/to/file')
print(result.output.ct_label)  # e.g., 'python'
print(result.output.score)     # e.g., 0.999

Related on TokRepo

• AI Tools for Security — Security-focused AI tools for scanning and analysis
• AI Tools for Automation — Automate file processing and content pipelines

Common pitfalls

• Assuming Magika replaces antivirus scanning. It identifies file types, not malicious content.
• Using file extensions as a fallback when Magika returns low confidence. Instead, treat low-confidence results as genuinely ambiguous files.
• Running Magika on extremely large files without understanding that it only reads a small prefix for classification.