Recon Tool: Maigret

Maigret

Recon is the most powerful and most important step when it comes to Offensive Security and Digital Forensics. It is a lot of time consuming, however finding the right tools and techniques to conduct them faster, is being smart.

Maigret by soxoj collects a dossier on a person by username only, checking for accounts on a huge number of sites and gathering all the available information from web pages. No API keys are required.

Currently supported by more than 2500 sites (full list), the search is launched against 500 popular sites in descending order of popularity by default. Also supported checking of Tor sites, I2P sites, and domains via DNS resolving.

Main Features

• Profile pages parsing, extraction of personal info, links to other profiles, etc.
• Recursive search by new usernames and other ids found
• Search by tags (site categories, countries)
• Censorship and captcha detection
• Requests retries

See full description of Maigret features in the documentation.

Installation

Maigret can be installed using pip, Docker, or simply can be launched from the cloned repo.

Standalone EXE-binaries for Windows are located in Releases section of GitHub repository.

Package installing

NOTE: Python 3.7 or higher and pip is required, Python 3.8 is recommended.

# install from pypi

pip3 install maigret

# usage

maigret username

Cloning a repository

# or clone and install manually

git clone https://github.com/soxoj/maigret && cd maigret

pip3 install -r requirements.txt

# usage

./maigret.py username

Docker

# official image

docker pull soxoj/maigret

# usage

docker run -v /mydir:/app/reports soxoj/maigret:latest username --html

# manual build

docker build -t maigret .

Usage Examples

# make HTML and PDF reports

maigret user --html --pdf

# search on sites marked with tags photo & dating

maigret user --tags photo,dating

# search for three usernames on all available sites

maigret user1 user2 user3 -a

Use maigret –help to get full options description. Also options are documented.

Demo with page parsing and recursive username search

Demo

Clone the repo from here: GitHub Link