analyzing-ransomware-leak-site-intelligence

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Monitor and analyze ransomware group data leak sites (DLS) to track victim postings, extract threat intelligence on group tactics, and assess sector-specific ransomware risk for proactive defense.

AI & Automation 12,642 stars 1468 forks Updated today Apache-2.0

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Skill Content

# Analyzing Ransomware Leak Site Intelligence ## Overview Ransomware groups operating under double-extortion models maintain data leak sites (DLS) on Tor hidden services where they post victim names, stolen data samples, and countdown timers to pressure payment. In H1 2025, 96 unique ransomware groups were active, listing approximately 535 victims per month. Monitoring these sites provides intelligence on active threat groups, targeted sectors, geographic patterns, and emerging ransomware families. This skill covers safely collecting DLS intelligence, extracting structured data, tracking group activity trends, and producing sector-specific risk assessments. ## When to Use - When investigating security incidents that require analyzing ransomware leak site intelligence - When building detection rules or threat hunting queries for this domain - When SOC analysts need structured procedures for this analysis type - When validating security monitoring coverage for related attack techniques ## Prerequisites - Python 3.9+ with `requests`, `beautifulsoup4`, `pandas`, `matplotlib` libraries - Tor proxy (SOCKS5) for accessing .onion sites or commercial DLS monitoring feeds - Understanding of ransomware double-extortion business model - Familiarity with major ransomware families (Qilin, Akira, LockBit, BlackCat, Clop) - Access to ransomware tracking feeds (Ransomwatch, RansomLook, DarkFeed) ## Key Concepts ### Double Extortion Model Modern ransomware groups encrypt victim data ...

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Author
mukul975
Repository
mukul975/Anthropic-Cybersecurity-Skills
Created
3 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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