Red Team tool for exfiltrating files from a target's Google Drive that you have access to, via Google's API.

Related tags

Deep LearningGD-Thief
Overview

GD-Thief

Red Team tool for exfiltrating files from a target's Google Drive that you(the attacker) has access to, via the Google Drive API. This includes includes all shared files, all files from shared drives, and all files from domain drives that the target has access to.

HOW TO

For an illustrated walkthrough, check out my blog post.

Create a new Google Cloud Platform (GCP) project

Steps to get the Google API Access Token needed for connecting to the API

  1. Create a burner Gmail/google account
  2. Login to said account
  3. Navigate to the Google Cloud Console
  4. Next to "Google Cloud Platform," click the "Select a project" Down arrow. A dialog listing current projects appears.
  5. Click New Project. The New Project screen appears.
  6. In the Project Name field, enter a descriptive name for your project.
  7. (Optional) To edit the Project ID, click Edit. The project ID can't be changed after the project is created, so choose an ID that meets your needs for the lifetime of the project.
  8. Click Create. The console navigates to the Dashboard page and your project is created within a few minutes.

Enable a Google Workspace API

  1. Next to "Google Cloud Platform," click the Down arrow and select the project you just created from the dropdown list.
  2. In the top-left corner, click Menu > APIs & Services.
  3. Click Enable APIs and Services. The "Welcome to API Library" page appears.
  4. In the search field, enter "Google Drive".
  5. Click the Google Drive API. The API page appears.
  6. Click Enable. The Overview page appears.

Configure OAuth Consent screen

  1. On the left side of the Overview page click Credentials. The credential page for your project appears.
  2. Click Configure Consent Screen. The "OAuth consent screen" screen appears.
  3. Click the External user type for your app.
  4. Click Create. A second "OAuth consent screen" screen appears.
  5. Fill out the form:
    • Enter an Application Name in the App name field
    • Enter your burner email address in the User support email field.
    • Enter your burner email address in the Developer contact information field.
  6. Click Save and Continue. The "Scopes" page appears.
  7. Click Add or Remove Scopes. The "Update selected scopes" page appears.
  8. Check all of the Google Drive scopes to use in the app. GD scopes cover 2 pages, so click the next page and ensure that you check them all.
  9. Click Update. A list of scopes for your app appears.
  10. Click Save and Continue. The "Edit app registration" page appears.
  11. Click Save and Continue. The "OAuth consent screen" appears.

Create a credential

  1. Click Create Credentials and select OAuth client ID. The "Create OAuth client ID" page appears.
  2. Click the Application type drop-down list and select Desktop Application.
  3. In the name field, type a name for the credential. This name is only shown in the Cloud Console.
  4. Click Create. The OAuth client created screen appears. This screen shows the Client ID and Client secret.
  5. Click OK. The newly created credential appears under "OAuth 2.0 Client IDs."
  6. Click the download button to the right of the newly-created OAuth 2.0 Client ID. This copies a client secret JSON file to your desktop. Note the location of this file.
  7. Rename the client secret JSON file to "credentials.json" and move it to the gd_thief/credentials directory.

Add the victim's Google account to the Application's Test Users

In order to be able to run this script against the victim, you will need to add their Google account to the Test Users list for the App you just created

  1. On the Left side of the screen click OAuth consent screen. You "OAuth Consent Screen" page appears.
  2. Under Test Users click the Add Users button.
  3. Enter the victim's Gmail address in the email address field.
  4. Click the save button.

First Time running gd_thief

Upon gaining access to a Target's Google account, you can run gd_thief

  1. The first time running gd_thief, the script opens a new window prompting you to authorize access to your data:
    1. If you are signed in to multiple Google accounts, you are asked to select one account to use for the authorization. Make sure you select the victim's Google account

Dependencies

Google API Libraries: pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib

Usage:

usage:
python3 gd_thief.py [-h] -m [{dlAll, dlDict[-d <DICTIONARY FILE PATH>]}
	[-t <THREAD COUNT>]

help:

This Module will connect to Google's API using an access token and exfiltrate files
from a target's Google Drive.  It will output exfiltrated files to the ./loot directory

arguments:
        -m [{dlAll, dlDict}],
                --mode [{dlAll, dlDict}]
                The mode of file download
                Can be "dlAll", "dlDict [-d <DICTIONARY FILE PATH>]", or... (More options to come)

optional arguments:
        -d <DICTIONARY FILE PATH>, --dict <DICTIONARY FILE PATH>
                        Path to the dictionary file. Mandatory with download mode"-m, --mode dlDict"
                        You can use the provided dictionary, per example: "-d ./dictionaries/secrets-keywords.txt"
        -t <THREAD COUNT>, --threads <THREAD COUNT>
                        Number of threads. (Too many could exceeed Google's rate limit threshold)

        -h, --help
                show this help message and exit

NOTES:

  • Setting the thread count too high will cause an HTTP 403 "Rate limit exceeded," indicating that the user has reached Google Drive API's maximum request rate.
    • The thread count limit vaires from machine to machine. I've set it to 250 on a Macbook Pro, while 250 was too high for my Windows 10 Desktop

REFERENCES:

TODO:

  1. Threading
  2. Error Checking
  3. Wordlist file content search and download
  4. File type download
  5. Snort Sensitive Data regex file content search and download
  6. Optical Character Recognition (OCR)

Special Thanks:

Thank you to my good friend Cedric Owens for helping me with the threading piece!

Owner
Antonio Piazza
Antonio Piazza
Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation(Unfinished)

Keras-FCN Fully convolutional networks and semantic segmentation with Keras. Models Models are found in models.py, and include ResNet and DenseNet bas

645 Dec 29, 2022
Tensorflow implementation of soft-attention mechanism for video caption generation.

SA-tensorflow Tensorflow implementation of soft-attention mechanism for video caption generation. An example of soft-attention mechanism. The attentio

Paul Chen 153 Nov 14, 2022
SenseNet is a sensorimotor and touch simulator for deep reinforcement learning research

SenseNet is a sensorimotor and touch simulator for deep reinforcement learning research

59 Feb 25, 2022
Some pvbatch (paraview) scripts for postprocessing OpenFOAM data

pvbatchForFoam Some pvbatch (paraview) scripts for postprocessing OpenFOAM data For every script there is a help message available: pvbatch pv_state_s

Morev Ilya 2 Oct 26, 2022
Text Summarization - WCN — Weighted Contextual N-gram method for evaluation of Text Summarization

Text Summarization WCN — Weighted Contextual N-gram method for evaluation of Text Summarization In this project, I fine tune T5 model on Extreme Summa

Aditya Shah 1 Jan 03, 2022
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis

O-CNN This repository contains the implementation of our papers related with O-CNN. The code is released under the MIT license. O-CNN: Octree-based Co

Microsoft 607 Dec 28, 2022
DRIFT is a tool for Diachronic Analysis of Scientific Literature.

About DRIFT is a tool for Diachronic Analysis of Scientific Literature. The application offers user-friendly and customizable utilities for two modes:

Rajaswa Patil 108 Dec 12, 2022
Official Implementation of Domain-Aware Universal Style Transfer

Domain Aware Universal Style Transfer Official Pytorch Implementation of 'Domain Aware Universal Style Transfer' (ICCV 2021) Domain Aware Universal St

KibeomHong 80 Dec 30, 2022
Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Improving Transferability of Representations via Augmentation-Aware Self-Supervision Accepted to NeurIPS 2021 TL;DR: Learning augmentation-aware infor

hankook 38 Sep 16, 2022
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

Kuan-Lin (Jason) Chen 2 Oct 02, 2022
A tool to analyze leveraged liquidity mining and find optimal option combination for hedging.

LP-Option-Hedging Description A Python program to analyze leveraged liquidity farming/mining and find the optimal option combination for hedging imper

Aureliano 18 Dec 19, 2022
Let Python optimize the best stop loss and take profits for your TradingView strategy.

TradingView Machine Learning TradeView is a free and open source Trading View bot written in Python. It is designed to support all major exchanges. It

Robert Roman 473 Jan 09, 2023
Repo for parser tensorflow(.pb) and tflite(.tflite)

tfmodel_parser .pb file is the format of tensorflow model .tflite file is the format of tflite model, which usually used in mobile devices before star

1 Dec 23, 2021
dualPC.R contains the R code for the main functions.

dualPC.R contains the R code for the main functions. dualPC_sim.R contains an example run with the different PC versions; it calls dualPC_algs.R whic

3 May 30, 2022
FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data

FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm that performs well i

0 Sep 06, 2022
A model to classify a piece of news as REAL or FAKE

Fake_news_classification A model to classify a piece of news as REAL or FAKE. This python project of detecting fake news deals with fake and real news

Gokul Stark 1 Jan 29, 2022
Veri Setinizi Yolov5 Formatına Dönüştürün

Veri Setinizi Yolov5 Formatına Dönüştürün! Bu Repo da Neler Var? Xml Formatındaki Veri Setini .Txt Formatına Çevirme Xml Formatındaki Dosyaları Silme

Kadir Nar 4 Aug 22, 2022
Real-time object detection on Android using the YOLO network with TensorFlow

TensorFlow YOLO object detection on Android Source project android-yolo is the first implementation of YOLO for TensorFlow on an Android device. It is

Nataniel Ruiz 624 Jan 03, 2023
This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by Divam Gupta, Wei Pu, Trenton Tabor, Jeff Schneider

SBEVNet: End-to-End Deep Stereo Layout Estimation This repository contains the code for "SBEVNet: End-to-End Deep Stereo Layout Estimation" paper by D

Divam Gupta 19 Dec 17, 2022
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21

MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen

Graph Analysis & Deep Learning Laboratory, GRAND 32 Jan 02, 2023