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Whomst

WHOMST — a tool that takes a Twitter (pre-2022) handle, attempts to identify an associated email address from the 2022 Twitter breach, then recursively searches the DeHashed API for associated content on that email. If an IP address is learnt, it is recursively searched as well, until emails and IPs are exhausted or limits are met

Install / Use

/learn @Splinters-io/Whomst
About this skill

Quality Score

0/100

Supported Platforms

Universal

README

WHOMST - A Sometimes useful Twitter Investigation Tool

CLASSIFICATION: UNCLASSIFIED // FOR NICE GUY USE ONLY

A proof-of-concept anti-fraud investigation tool that uses the DeHashed API to map relationships between Twitter aliases, email addresses, and IP addresses found in data breaches available via Dehashed API (you'll need a Key).

Legal Notice

This tool is for defensive security and anti-fraud purposes only. Users must:

  • Only investigate accounts with proper authorization
  • Comply with all applicable laws and regulations
  • Respect privacy and data protection laws
  • Use responsibly and ethically

Features

  • 2022 Twitter Breach Entry Point: Only investigates targets found in the 2022 Twitter breach database (BBC report)
  • Recursive Mapping: Maps Twitter alias → email → IP → other emails through breach data
  • Relationship Mapping: Builds graph of connections between identities discovered through recursion
  • Intelligence Briefing: Generates professional intelligence reports on discovered relationships
  • Password Analysis: Extracts passwords and hashes, organizes by hash type
  • Hashcat Integration: Generates ready-to-use hashcat commands and wordlists
  • Interactive Visualization: Web-based graph explorer with filtering
  • Logic Logging: Tracks investigation flow and decisions through recursion depth

Quick Start

1. Download Pre-compiled Binary

Download the appropriate binary for your platform from the releases/ directory:

  • Windows: whomst-windows-x64.exe
  • macOS Intel: whomst-macos-intel
  • macOS Apple Silicon: whomst-macos-arm64
  • Linux: whomst-linux-x64

2. Configure

cd src
cp config.yaml.example config.yaml
# Edit config.yaml with your DeHashed credentials

3. Run Investigation

# Using pre-compiled binary
./whomst-<platform> -target <twitter_username>

# Or build from source
cd src
go build -o whomst
./whomst -target <twitter_username>

Repository Structure

whomst/
├── README.md              # This file - main project overview
├── docs/                  # Complete documentation
│   ├── SETUP.md          # Detailed setup guide
│   ├── SECURITY.md       # Security policy and best practices
│   ├── RELEASES.md       # Platform guide for binaries
│   └── ...               # Additional documentation
├── src/                  # Source code
│   ├── main.go           # CLI entry point
│   ├── config.yaml.example # Configuration template
│   ├── go.mod            # Go module definition
│   └── ...               # Core implementation files
└── releases/             # Pre-compiled binaries
    ├── whomst-windows-x64.exe
    ├── whomst-macos-arm64
    ├── whomst-linux-x64
    └── ...               # All platform binaries

Documentation

  • SETUP.md - Complete installation and configuration guide
  • SECURITY.md - Security policy and legal considerations
  • RELEASES.md - Platform-specific binary usage guide

How It Works

flowchart TD
    A[Twitter Username Input] --> B{Search DeHashed for Username}
    B --> C{Found in 2022 Twitter Breach?}
    C -->|No| D[Investigation Terminated]
    C -->|Yes| E[Extract Initial Data]
    E --> F[Emails, IPs, Names, Phones]
    
    F --> G{Recursion Depth < Max?}
    G -->|No| M[Generate Outputs]
    G -->|Yes| H[Search Each Email in DeHashed]
    
    H --> I[Extract Connected Data]
    I --> J{IP Recursion Enabled?}
    J -->|Yes| K[Search Each IP in DeHashed]
    J -->|No| L[Skip IP Search]
    K --> I
    L --> I
    
    I --> N{More Entities to Process?}
    N -->|Yes| G
    N -->|No| M
    
    M --> O[Intelligence Report]
    M --> P[Relationship Graph]
    M --> Q[Interactive Visualization]
    M --> R[Hashcat Files]
    
    O --> S[Complete Investigation]
    P --> S
    Q --> S
    R --> S
    
    style C fill:#ff6b6b
    style G fill:#4ecdc4
    style J fill:#45b7d1
    style M fill:#96ceb4

Investigation Flow

  1. Twitter Breach Entry: Searches DeHashed for username in 2022 Twitter breach
  2. Initial Validation: Only proceeds if target exists in 2022 Twitter breach database, takes the email, searches the whole dataset on that email
  3. Data Extraction: Extracts emails, IPs, usernames, names, phones, addresses from Twitter breach entry
  4. Recursive Mapping:
    • Searches each discovered email address for other breaches
    • Searches each discovered IP address for other accounts
    • Maps IP addresses to additional email addresses
    • Continues recursion to configurable depth
  5. Intelligence Analysis: Builds comprehensive graph of all discovered relationships
  6. Report Generation: Creates intelligence briefing documenting the complete investigation thread

Building from Source

Prerequisites

  • Go 1.21 or later
  • DeHashed API account with credits

Build Instructions

cd src
go mod download
go build -o whomst

API Usage & Costs

  • Each search consumes DeHashed credits based on results returned
  • Monitor usage at https://www.dehashed.com/
  • Default rate limit: 1 second between requests
  • Deeper recursion = more API calls = higher costs

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes in the src/ directory
  4. Test thoroughly
  5. Submit a pull request

License

This tool is provided under the MIT License with additional terms for security tools. See LICENSE for details.

Disclaimer

This tool is a proof of concept for nice guy investigation. Users are responsible for ensuring compliance with all applicable laws and regulations. The authors assume no liability for misuse.


CLASSIFICATION: UNCLASSIFIED // FOR OFFICIAL USE ONLY

Related Skills

View on GitHub
GitHub Stars5
CategoryContent
Updated2mo ago
Forks1

Languages

Go

Security Score

70/100

Audited on Jan 6, 2026

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