astroida / pagodo

pagodo (Passive Google Dork) - Automate Google Hacking Database scraping

PaGoDo

Introduction

The goal of this project was to develop a passive Google dork script to collect potentially vulnerable web pages and applications on the Internet. There are 2 parts. The first is ghdb_scraper.py that retrieves Google Dorks and the second portion is pagodo.py that leverages the information gathered by ghdb_scraper.py.

What are Google Dorks?

The awesome folks at Offensive Security maintain 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.

Installation

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

git clone https://github.com/opsdisk/pagodo.git
cd pagodo
virtualenv -p python3 .venv  # If using a virtual environment.
source .venv/bin/activate  # If using a virtual environment.
pip3 install -r requirements.txt

ghdb_scraper.py

To start off, pagodo.py needs a list of all the current Google dorks. A datetimestamped file with the Google dorks is also provded in the repo. Fortunately, the entire database can be pulled back with 1 GET request using ghdb_scraper.py. You can dump the individual dorks to a text file, or the entire json blob if you want more contextual data about the dork.

To run it:

python3 ghdb_scraper.py -j -s

pagodo.py

Now that a file with the most recent Google dorks exists, it can be fed into pagodo.py using the -g switch to start collecting potentially vulnerable public applications. pagodo.py leverages the google python library to search Google for sites with the Google dork, such as:

intitle:"ListMail Login" admin -demo

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

site:example.com  

Performing ~4600 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. In order to make the search queries appear more human, a couple of enhancements have been made. A pull request was made and accepted by the maintainer of the Python google module to allow for User-Agent randomization in the Google search queries. This feature is available in 1.9.3 and allows you to randomize the different user agents used for each search. This emulates the different browsers used in a large corporate environment.

The second enhancement focuses on randomizing the time between search queries. A minimum delay is specified using the -e option and a jitter factor is used to add time on to the minimum delay number. A list of 50 jitter times is created and one is randomly appended to the minimum delay time for each Google dork search.

# Create an array of jitter values to add to delay, favoring longer search times.
self.jitter = numpy.random.uniform(low=self.delay, high=jitter * self.delay, size=(50,))

Latter in the script, a random time is selected from the jitter array and added to the delay.

pause_time = self.delay + random.choice(self.jitter)

Experiment with the values, but the defaults successfully worked without Google blocking my IP. Note that it could take a few days (3 on average) to run so be sure you have the time.

To run it:

python3 pagodo.py -d example.com -g dorks.txt -l 50 -s -e 35.0 -j 1.1

Conclusion

Comments, suggestions, and improvements are always welcome. Be sure to follow @opsdisk on Twitter for the latest updates.