Analyzing Ransomware Leak Site IntelligenceSAFE

817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 29 security domains ·

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: analyzing-ransomware-leak-site-intelligence
description: Safely monitor ransomware group Tor-hosted data leak sites (DLS) to collect and extract structured victim posting data, track group activity trends over time, and produce sector- and geography-specific ransomware risk assessments. Use when performing threat intelligence gathering on active ransomware groups or building proactive defense reporting from double-extortion leak-site activity.
domain: cybersecurity
subdomain: threat-intelligence
tags:
- ransomware
- leak-site
- data-leak
- extortion
- threat-intelligence
- leak-site-monitoring
- dls
- victim-tracking
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
mitre_attack:
- T1657
- T1486
- T1567.002
- T1591
mitre_f3:
  version: '1.1'
  tactics:
  - monetization
  - reconnaissance
  techniques:
  - id: F1018
    name: Convert to Cryptocurrency
    tactic: monetization
    source: f3
  - id: F1029
    name: Gather Customer Information
    tactic: reconnaissance
    source: f3
  - id: T1593
    name: Search Open Websites/Domains
    tactic: reconnaissance
    source: attack
  - id: F1025.003
    name: 'Electronic Funds Transfer: Wire Transfer'
    tactic: monetization
    source: f3
---
# 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 AND exfiltrate it before encryption. Leak sites serve as public pressure: victims are listed with a countdown timer, partial data samples, and file trees. If ransom is not paid, full data is published. Some groups have moved to triple extortion, adding DDoS threats or contacting victims' customers directly.

### DLS Intelligence Value

Leak sites provide: victim identification (company name, sector, country), attack timeline (when listed, deadline, data published), data volume estimates, group capability assessment (sectors targeted, attack frequency, operational tempo), and trend analysis (new groups emerging, groups rebranding, law enforcement takedowns).

### Safe Collection Practices

Never directly access DLS sites in a production environment. Use purpose-built monitoring services (Ransomwatch, DarkFeed, KELA, Flashpoint), Tor-isolated research VMs, commercial threat intelligence platforms, or community-maintained datasets. All analysis should be conducted in isolated environments with proper authorization.

## Workflow

### Step 1: Ingest Ransomware Leak Site Data from Public Feeds

```python
import requests
import json
import pandas as pd
from datetime import datetime, timedelta
from collections import Counter

class RansomwareIntelCollector:
    """Collect ransomware DLS intelligence from public tracking sources."""

    RANSOMWATCH_API = "https://raw.githubusercontent.com/joshhighet/ransomwatch/main/posts.json"
    RANSOMWATCH_GROUPS = "https://raw.githubusercontent.com/joshhighet/ransomwatch/main/groups.json"

    def __init__(self):
        self.posts = []
        self.groups = []

    def fetch_ransomwatch_data(self):
        """Fetch ransomware victim posts from ransomwatch."""
        resp = requests.get(self.RANSOMWATCH_API, timeout=30)
        if resp.status_code == 200:
            self.posts = resp.json()
            print(f"[+] Loaded {len(self.posts)} victim posts from ransomwatch")
        else:
            print(f"[-] Failed to fetch posts: {resp.status_code}")

        resp = requests.get(self.RANSOMWATCH_GROUPS, timeout=30)
        if resp.status_code == 200:
            self.groups = resp.json()
            print(f"[+] Loaded {len(self.groups)} ransomware group profiles")

        return self.posts

    def get_recent_victims(self, days=30):
        """Get victims posted in the last N days."""
        cutoff = datetime.now() - timedelta(days=days)
        recent = []
        for post in self.posts:
            try:
                discovered = datetime.fromisoformat(
                    post.get("discovered", "").replace("Z", "+00:00")
                )
                if discovered.replace(tzinfo=None) >= cutoff:
                    recent.append(post)
            except (ValueError, TypeError):
                continue
        print(f"[+] {len(recent)} victims in last {days} days")
        return recent

    def get_group_activity(self, group_name):
        """Get all posts by a specific ransomware group."""
        group_posts = [
            p for p in self.posts
            if p.get("group_name", "").lower() == group_name.lower()
        ]
        print(f"[+] {group_name}: {len(group_posts)} total victims")
        return group_posts

c

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

What the source does

Filesystem

none-observed

Network

declared (2 observation(s))

Shell

none-observed

Dependencies

pinned

Secrets in source

none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.