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The Endorser

An OSINT tool that allows you to draw out relationships between people on LinkedIn via endorsements/skills.

Check out the example (digraph), which is based on mine and my friends (David Prince) LinkedIn profile. By glancing at the visualisation you can easily see, by the number of "arrows", there is some sort of relationship between us and "Zoë Rose" (in this case we used to work together). If I was carrying out an investigation I would focus my efforts towards her next.

Example

Due to the way LinkedIn's privacy settings work this tool works best when your target is within your 3rd degree network or higher. Using a LinkedIn Premium or Recruiter account will allow you to map targets outside of your network.

Installation

All you need it Python 3.4+ and do the following:

  1. git clone https://github.com/eth0izzle/the-endorser.git
  2. sudo pip3 install -r requirements.txt
  3. Install Graphviz from https://www.graphviz.org/download/ or via your package manager, e.g. apt-get install graphviz or brew install graphviz or choco install graphiz
  4. Download ChromeDriver for your platform (requires Chrome) and place in ./drivers. Alternatively you can use PhantomJS and launch with the --driver phantomjs flag (note phantomjs is 8x slower).
  5. Setup your LinkedIn credentials in config.yaml
  6. python3 the-endorser.py <profile1> <profile2> <profile3> <etc..> <etc..>

Usage

usage: python the-endorser.py https://www.linkedin.com/in/user1 https://www.linkedin.com/in/user2

Maps out relationships between peoples endorsements on LinkedIn.

positional arguments:
  profiles              Space separated list of LinkedIn profile URLs to map

optional arguments:
  -h, --help            show this help message and exit
  --config_file CONFIG_FILE
                        Specify the path of the config.yaml file (default:
                        ./the-endorser/config.yaml)
  --driver DRIVER       Selenium WebDriver to use to parse the webpages:
                        chromedriver, phantomjs (default: chromedriver)
  --output OUTPUT       Output module to visualise the relationships: digraph,
                        stdout (default: digraph)
  --log LOG             Path of log file. None for stdout. (default: None)
  --log-level LOG_LEVEL
                        Logging output level: DEBUG, INFO, WARNING, ERROR.
                        (default: INFO)

Outputs

The Endorser is "modular" in the sense that it can output and visualise the data in different ways. An output module just needs one method: def run(profiles)

Currently there is only one output module (digraph). In the future I plan to add modules for Maltego and Plot.ly - but feel free to get involved!

Digraph

It's best to read the graph from right-to-left, identifying people that have a large collection of "arrows" from multiple profiles (different colours). Square box = skill, ellipse = person.

Contributing

Check out the issue tracker and see what tickles your fancy.

  1. Fork it, baby!
  2. Create a feature branch: git checkout -b my-new-feature
  3. Create your super-awesome feature!
  4. Commit your changes: git commit -am 'Add some feature'
  5. Push to the branch: git push origin my-new-feature
  6. Submit a pull request

License

MIT. See LICENSE