TargetAllDomainObjects - A python wrapper to run a command on against all users/computers/DCs of a Windows Domain

Overview

TargetAllDomainObjects

A python wrapper to run a command on against all users/computers/DCs of a Windows Domain
GitHub release (latest by date)

Features

  • Automatically gets the list of all users/computers/DCs from the domain controller's LDAP.
  • Multithreaded command execution.
  • Saves the output of the commands to a file.

Usage

$ ./TargetAllDomainObjects.py -h          
Impacket v0.9.25.dev1+20220105.151306.10e53952 - Copyright 2021 SecureAuth Corporation

usage: TargetAllDomainObjects.py [-h] -c COMMAND [-ts] [--use-ldaps] [-q] [-debug] [-colors] [-t THREADS] [-o OUTPUT_FILE] --dc-ip ip address [-d DOMAIN]
                                 [-u USER] [--no-pass | -p PASSWORD | -H [LMHASH:]NTHASH | --aes-key hex key] [-k]
                                 targetobject

Wrapper to run a command on against all users/computers/DCs of a Windows Domain.

positional arguments:
  targetobject          Target object (user, computer, domaincontroller)

optional arguments:
  -h, --help            show this help message and exit
  -c COMMAND, --command COMMAND
                        Command to launch, with {target} where the target should be placed.
  -ts                   Adds timestamp to every logging output
  --use-ldaps           Use LDAPS instead of LDAP
  -q, --quiet           show no information at all
  -debug                Debug mode
  -colors               Colored output mode
  -t THREADS, --threads THREADS
                        Number of threads (default: 5)
  -o OUTPUT_FILE, --output-file OUTPUT_FILE
                        Output file to store the results in. (default: shares.json)

authentication & connection:
  --dc-ip ip address    IP Address of the domain controller or KDC (Key Distribution Center) for Kerberos. If omitted it will use the domain part (FQDN)
                        specified in the identity parameter
  -d DOMAIN, --domain DOMAIN
                        (FQDN) domain to authenticate to
  -u USER, --user USER  user to authenticate with

  --no-pass             Don't ask for password (useful for -k)
  -p PASSWORD, --password PASSWORD
                        Password to authenticate with
  -H [LMHASH:]NTHASH, --hashes [LMHASH:]NTHASH
                        NT/LM hashes, format is LMhash:NThash
  --aes-key hex key     AES key to use for Kerberos Authentication (128 or 256 bits)
  -k, --kerberos        Use Kerberos authentication. Grabs credentials from .ccache file (KRB5CCNAME) based on target parameters. If valid credentials
                        cannot be found, it will use the ones specified in the command line
                      

Demo

Contributing

Pull requests are welcome. Feel free to open an issue if you want to add other features.

You might also like...
A Pytorch Implementation of [Source data‐free domain adaptation of object detector through domain

A Pytorch Implementation of Source data‐free domain adaptation of object detector through domain‐specific perturbation Please follow Faster R-CNN and

Fake-user-agent-traffic-geneator - Python CLI Tool to generate fake traffic against URLs with configurable user-agents
Fake-user-agent-traffic-geneator - Python CLI Tool to generate fake traffic against URLs with configurable user-agents

Fake traffic generator for Gartner Demo Generate fake traffic to URLs with custo

Code for the paper: Adversarial Training Against Location-Optimized Adversarial Patches. ECCV-W 2020.

Adversarial Training Against Location-Optimized Adversarial Patches arXiv | Paper | Code | Video | Slides Code for the paper: Sukrut Rao, David Stutz,

Protect against subdomain takeover
Protect against subdomain takeover

domain-protect scans Amazon Route53 across an AWS Organization for domain records vulnerable to takeover deploy to security audit account scan your en

A certifiable defense against adversarial examples by training neural networks to be provably robust
A certifiable defense against adversarial examples by training neural networks to be provably robust

DiffAI v3 DiffAI is a system for training neural networks to be provably robust and for proving that they are robust. The system was developed for the

Defending graph neural networks against adversarial attacks (NeurIPS 2020)
Defending graph neural networks against adversarial attacks (NeurIPS 2020)

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks Authors: Xiang Zhang ([email protected]), Marinka Zitnik ([email protected].

G-NIA model from
G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)

Single Node Injection Attack against Graph Neural Networks This repository is our Pytorch implementation of our paper: Single Node Injection Attack ag

RL-driven agent playing tic-tac-toe on starknet against challengers.

tictactoe-on-starknet RL-driven agent playing tic-tac-toe on starknet against challengers. GUI reference: https://pythonguides.com/create-a-game-using

Cards Against Humanity AI

cah-ai This is a Cards Against Humanity AI implemented using a pre-trained Semantic Search model. How it works A player is described by a combination

Releases(1.1)
Owner
Podalirius
Security Researcher 🕵️‍♂️ | Speaker 📣
Podalirius
Official PyTorch Implementation of Mask-aware IoU and maYOLACT Detector [BMVC2021]

The official implementation of Mask-aware IoU and maYOLACT detector. Our implementation is based on mmdetection. Mask-aware IoU for Anchor Assignment

Kemal Oksuz 46 Sep 29, 2022
T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time

T-LOAM: Truncated Least Squares Lidar-only Odometry and Mapping in Real-Time The first Lidar-only odometry framework with high performance based on tr

Pengwei Zhou 183 Dec 01, 2022
Official PyTorch implementation of the paper "Deep Constrained Least Squares for Blind Image Super-Resolution", CVPR 2022.

Deep Constrained Least Squares for Blind Image Super-Resolution [Paper] This is the official implementation of 'Deep Constrained Least Squares for Bli

MEGVII Research 141 Dec 30, 2022
LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation

LEDNet: A Lightweight Encoder-Decoder Network for Real-time Semantic Segmentation Table of Contents: Introduction Project Structure Installation Datas

Yu Wang 492 Dec 02, 2022
A custom DeepStack model that has been trained detecting ONLY the USPS logo

This repository provides a custom DeepStack model that has been trained detecting ONLY the USPS logo. This was created after I discovered that the Deepstack OpenLogo custom model I was using did not

Stephen Stratoti 9 Dec 27, 2022
Python版OpenCVのTracking APIのサンプルです。DaSiamRPNアルゴリズムまで対応しています。

OpenCV-Object-Tracker-Sample Python版OpenCVのTracking APIのサンプルです。   Requirement opencv-contrib-python 4.5.3.56 or later Algorithm 2021/07/16時点でOpenCVには以

KazuhitoTakahashi 36 Jan 01, 2023
An example of time series augmentation methods with Keras

Time Series Augmentation This is a collection of time series data augmentation methods and an example use using Keras. News 2020/04/16: Repository Cre

九州大学 ヒューマンインタフェース研究室 229 Jan 02, 2023
Computer Vision and Pattern Recognition, NUS CS4243, 2022

CS4243_2022 Computer Vision and Pattern Recognition, NUS CS4243, 2022 Cloud Machine #1 : Google Colab (Free GPU) Follow this Notebook installation : h

Xavier Bresson 142 Dec 15, 2022
cisip-FIRe - Fast Image Retrieval

Fast Image Retrieval (FIRe) is an open source image retrieval project release by Center of Image and Signal Processing Lab (CISiP Lab), Universiti Malaya. This project implements most of the major bi

CISiP Lab 39 Nov 25, 2022
A Lighting Pytorch Framework for Recommendation System, Easy-to-use and Easy-to-extend.

Torch-RecHub A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend. 安装 pip install torch-rechub 主要特性 scikit-learn风格易用

Mincai Lai 67 Jan 04, 2023
Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch

Segformer - Pytorch Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch. Install $ pip install segformer-pytorch

Phil Wang 208 Dec 25, 2022
A Python package for generating concise, high-quality summaries of a probability distribution

GoodPoints A Python package for generating concise, high-quality summaries of a probability distribution GoodPoints is a collection of tools for compr

Microsoft 28 Oct 10, 2022
HiFT: Hierarchical Feature Transformer for Aerial Tracking (ICCV2021)

HiFT: Hierarchical Feature Transformer for Aerial Tracking Ziang Cao, Changhong Fu, Junjie Ye, Bowen Li, and Yiming Li Our paper is Accepted by ICCV 2

Intelligent Vision for Robotics in Complex Environment 55 Nov 23, 2022
This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text"

Iconary This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text". It includes the

AI2 6 May 24, 2022
a project for 3D multi-object tracking

a project for 3D multi-object tracking

155 Jan 04, 2023
Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates

Safe Control for Black-box Dynamical Systems via Neural Barrier Certificates Installation Clone the repository: git clone https://github.com/Zengyi-Qi

Zengyi Qin 3 Oct 18, 2022
Semiconductor Machine learning project

Wafer Fault Detection Problem Statement: Wafer (In electronics), also called a slice or substrate, is a thin slice of semiconductor, such as a crystal

kunal suryawanshi 1 Jan 15, 2022
Implementing DeepMind's Fast Reinforcement Learning paper

Fast Reinforcement Learning This is a repo where I implement the algorithms in the paper, Fast reinforcement learning with generalized policy updates.

Marcus Chiam 6 Nov 28, 2022
FB-tCNN for SSVEP Recognition

FB-tCNN for SSVEP Recognition Here are the codes of the tCNN and FB-tCNN in the paper "Filter Bank Convolutional Neural Network for Short Time-Window

Wenlong Ding 12 Dec 14, 2022
This is an implementation for the CVPR2020 paper "Learning Invariant Representation for Unsupervised Image Restoration"

Learning Invariant Representation for Unsupervised Image Restoration (CVPR 2020) Introduction This is an implementation for the paper "Learning Invari

GarField 88 Nov 07, 2022