CrossLinked

CrossLinked is a LinkedIn enumeration tool that uses search engine scraping to collect valid employee names from an organization. This technique provides accurate results without the use of API keys, credentials, or accessing LinkedIn directly!

Sponsors

🚩 Consider sponsoring this project to ensure the latest improvements, have your company logo listed here, and get priority support - visit github.com/sponsors/m8sec

Install

Install the last stable release from PyPi:

pip3 install crosslinked

Or, install the most recent code from GitHub:

git clone https://github.com/m8sec/crosslinked
cd crosslinked
python3 setup.py install

Prerequisite

CrossLinked assumes the organization's account naming convention has already been identified. This is required for execution and should be added to the CMD args based on your expected output. See the Naming Format and Example Usage sections below:

Naming Format

{f}.{last}              = j.smith
{first.{last}           = john.smith
CMP\{first}{l}          = CMP\johns
{f}{last}@company.com   = [email protected]

Still Stuck? Metadata is always a good place to check for hidden information such as account naming convention. see PyMeta for more.

Search

By default, CrossLinked will use google and bing search engines to identify employees of the target organization. After execution, two files (names.txt & names.csv) will appear in the current directory, unless modified in the CMD args.

• names.txt - List of unique user accounts in the specified format.
• names.csv - Raw search data. See the Parse section below for more.

Example Usage

python3 crosslinked.py -f '{first}.{last}@domain.com' company_name

python3 crosslinked.py -f 'domain\{f}{last}' -t 15 -j 2 company_name

Note: For best results, use the company name as it appears on LinkedIn "Target Company" not the domain name.

Screenshots

Parse

💥 New Feature 💥

Account naming convention changed after execution and now your hitting CAPTCHA requests? No Problem!

CrossLinked v0.2.0 now includes a names.csv output file, which stores all scraping data including: first name, last name, job title, and url. This can be ingested and parsed to reformat user accounts as needed.

Example Usage

python3 crosslinked.py -f '{f}{last}@domain.com' names.csv

Screenshots

Additional Options

Proxy Rotation

The latest version of CrossLinked provides proxy support to rotate source addresses. Users can input a single proxy with --proxy 127.0.0.1:8080 or use multiple via --proxy-file proxies.txt.

> cat proxies.txt
127.0.0.1:8080
socks4://111.111.111.111
socks5://222.222.222.222

> python3 crosslinked.py --proxy-file proxies.txt -f '{first}.{last}@company.com' -t 10 "Company"

Note: HTTP/S proxies can be added by IP:Port notation. However, socks proxies will require a socks4:// or socks5:// prefix.

Usage

positional arguments:
  company_name        Target company name

optional arguments:
  -h, --help          show help message and exit
  -t TIMEOUT          Max timeout per search (Default=15)
  -j JITTER           Jitter between requests (Default=1)

Search arguments:
  --search ENGINE     Search Engine (Default='google,bing')

Output arguments:
  -f NFORMAT          Format names, ex: 'domain\{f}{last}', '{first}.{last}@domain.com'
  -o OUTFILE          Change name of output file (omit_extension)

Proxy arguments:
  --proxy PROXY       Proxy requests (IP:Port)
  --proxy-file PROXY  Load proxies from file for rotation

Contribute

Contribute to the project by:

• Like and share the tool!
• Create an issue to report any problems or, better yet, initiate a PR.
• Reach out with any potential features or improvements @m8sec.