Malicious and Safe URLs: Labeled Database for Machine Learning

This database of malicious and safe URLs was created by SaibaDev and last updated in January 2026. It provides a collection of web addresses categorized for security-related machine learning tasks.

Use Cases

• Training a binary classifier to distinguish between safe and malicious URLs
• Feature extraction from URL strings to identify phishing patterns
• Benchmarking URL filtering algorithms against a 2026 dataset

Strengths

• Updated in 2026
• Binary classification labels for malicious vs safe categories

Limitations

• Unknown record count
• Lack of documented schema or column names
• Unknown source of URL labels

Provenance

Source

SaibaDev via GitHub

Freshness

Updated as recently as January 2026.