/Btech_PyDoSc

Python Reconnaisance using Google Dorks and Web Scraping

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

PyDoSc - Passive Google Dork (OSint) and Scrap

Introduction

pydosc automates Google searching for potentially vulnerable web pages and applications on the Internet. It replaces manually performing Google dork searches with a web GUI browser.

There are 2 parts. The first is ghdb_scraper.py that retrieves the latest Google dorks and the second portion is pydosc.py that leverages the information gathered by ghdb_scraper.py.

The core Google search library now uses the more flexible yagooglesearch instead of googlesearch. Check out the yagooglesearch README for a more in-depth explanation of the library differences and capabilities.

This version of pydosc also supports native HTTP(S) and SOCKS5 application support, so no more wrapping it in a tool like proxychains4 if you need proxy support. You can specify multiple proxies to use in a round-robin fashion by providing a comma separated string of proxies using the -p switch.

What are Google dorks?

Offensive Security maintains the Google Hacking Database (GHDB) found here: https://www.exploit-db.com/google-hacking-database. It is a collection of Google searches, called dorks, that can be used to find potentially vulnerable boxes or other juicy info that is picked up by Google's search bots.

Terms and Conditions

The terms and conditions for pydosc are the same terms and conditions found in yagooglesearch.

This code is supplied as-is and you are fully responsible for how it is used. Scraping Google Search results may violate their Terms of Service. Another Python Google search library had some interesting information/discussion on it:

Google's preferred method is to use their API.

Installation

Scripts are written for Python 3.6+. Clone the git repository and install the requirements.

git clone https://github.com/opsdisk/pydosc.git
cd pydosc
virtualenv -p python3.7 .venv  # If using a virtual environment.
source .venv/bin/activate  # If using a virtual environment.
pip install -r requirements.txt

ghdb_scraper.py

To start off, pydosc.py needs a list of all the current Google dorks. The repo contains a dorks/ directory with the current dorks when the ghdb_scraper.py was last run. It's advised to run ghdb_scraper.py to get the freshest data before running pydosc.py. The dorks/ directory contains:

  • the all_google_dorks.txt file which contains all the Google dorks, one per line
  • the all_google_dorks.json file which is the JSON response from GHDB
  • Individual category dorks

Dork categories:

categories = {
    1: "Footholds",
    2: "File Containing Usernames",
    3: "Sensitives Directories",
    4: "Web Server Detection",
    5: "Vulnerable Files",
    6: "Vulnerable Servers",
    7: "Error Messages",
    8: "File Containing Juicy Info",
    9: "File Containing Passwords",
    10: "Sensitive Online Shopping Info",
    11: "Network or Vulnerability Data",
    12: "Pages Containing Login Portals",
    13: "Various Online devices",
    14: "Advisories and Vulnerabilities",
}

Using ghdb_scraper.py as a script

Write all dorks to all_google_dorks.txt, all_google_dorks.json, and individual categories if you want more contextual data about each dork.

python ghdb_scraper.py -s -j -i

Using ghdb_scraper as a module

The ghdb_scraper.retrieve_google_dorks() function returns a dictionary with the following data structure:

ghdb_dict = {
    "total_dorks": total_dorks,
    "extracted_dorks": extracted_dorks,
    "category_dict": category_dict,
}

Using a Python shell (like python or ipython) to explore the data:

import ghdb_scraper

dorks = ghdb_scraper.retrieve_google_dorks(save_all_dorks_to_file=True)
dorks.keys()
dorks["total_dorks"]

dorks["extracted_dorks"]

dorks["category_dict"].keys()

dorks["category_dict"][1]["category_name"]

pydosc.py

Using pydosc.py as a script

python pydosc.py -d example.com -g dorks.txt

Using pydosc as a module

The pydosc.pydosc.go() function returns a dictionary with the data structure below (dorks used are made up examples):

{
    "dorks": {
        "inurl:admin": {
            "urls_size": 3,
            "urls": [
                "https://github.com/marmelab/ng-admin",
                "https://github.com/settings/admin",
                "https://github.com/akveo/ngx-admin",
            ],
        },
        "inurl:gist": {
            "urls_size": 3,
            "urls": [
                "https://gist.github.com/",
                "https://gist.github.com/index",
                "https://github.com/defunkt/gist",
            ],
        },
    },
    "initiation_timestamp": "2021-08-27T11:35:30.638705",
    "completion_timestamp": "2021-08-27T11:36:42.349035",
}

Using a Python shell (like python or ipython) to explore the data:

import pydosc

pg = pydosc.pydosc(
    google_dorks_file="dorks.txt",
    domain="github.com",
    max_search_result_urls_to_return_per_dork=3,
    save_pydosc_results_to_json_file=True,
    save_urls_to_file=True,
    verbosity=5,
)
pydosc_results_dict = pg.go()

pydosc_results_dict.keys()

pydosc_results_dict["initiation_timestamp"]

pydosc_results_dict["completion_timestamp"]

for key,value in pydosc_results_dict["dorks"].items():
    print(f"dork: {key}")
    for url in value["urls"]:
        print(url)

Tuning Results

Scope to a specific domain

The -d switch can be used to scope the results to a specific domain and functions as the Google search operator:

site:github.com

Wait time between Google dork searchers

  • -i - Specify the minimum delay between dork searches, in seconds. Don't make this too small, or your IP will get HTTP 429'd quickly.
  • -x - Specify the maximum delay between dork searches, in seconds. Don't make this too big or the searches will take a long time.

The values provided by -i and -x are used to generate a list of 20 randomly wait times, that are randomly selected between each different Google dork search.

Number of results to return

-m - The total max search results to return per Google dork. Each Google search request can pull back at most 100 results at a time, so if you pick -m 500, 5 separate search queries will have to be made for each Google dork search, which will increase the amount of time to complete.

Google is blocking me!

Performing 7300+ search requests to Google as fast as possible will simply not work. Google will rightfully detect it as a bot and block your IP for a set period of time. One solution is to use a bank of HTTP(S)/SOCKS proxies and pass them to pydosc

Native proxy support

Pass a comma separated string of proxies to pydosc using the -p switch.

python pydosc.py -g dorks.txt -p http://myproxy:8080,socks5h://127.0.0.1:9050,socks5h://127.0.0.1:9051

You could even decrease the -i and -x values because you will be leveraging different proxy IPs. The proxies passed to pydosc are selected by round robin.

proxychains4 support

Another solution is to use proxychains4 to round robin the lookups.

Install proxychains4

apt install proxychains4 -y

Edit the /etc/proxychains4.conf configuration file to round robin the look ups through different proxy servers. In the example below, 2 different dynamic socks proxies have been set up with different local listening ports (9050 and 9051).

vim /etc/proxychains4.conf
round_robin
chain_len = 1
proxy_dns
remote_dns_subnet 224
tcp_read_time_out 15000
tcp_connect_time_out 8000
[ProxyList]
socks4 127.0.0.1 9050
socks4 127.0.0.1 9051

Throw proxychains4 in front of the pydosc.py script and each request lookup will go through a different proxy (and thus source from a different IP).

proxychains4 python pydosc.py -g dorks/all_google_dorks.txt -o -s

Note that this may not appear natural to Google if you:

  1. Simulate "browsing" to google.com from IP #1
  2. Make the first search query from IP #2
  3. Simulate clicking "Next" to make the second search query from IP #3
  4. Simulate clicking "Next to make the third search query from IP #1

For that reason, using the built in -p proxy support is preferred because, as stated in the yagooglesearch documentation, the "provided proxy is used for the entire life cycle of the search to make it look more human, instead of rotating through various proxies for different portions of the search."

Reference for Dorks

https://securitytrails.com/blog/google-hacking-techniques

License

Distributed under the GNU General Public License v3.0. See LICENSE for more information.

Contact

Twitter: @Cyberkid Adithya

LinkedIn: @Adithya E S

Project Link: https://github.com/CyberkidAdithya/PyDoSc