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
An end-to-end PyTorch framework for image and video classification

What's New: March 2021: Added RegNetZ models November 2020: Vision Transformers now available, with training recipes! 2020-11-20: Classy Vision v0.5 R

Facebook Research 1.5k Dec 31, 2022
Improving 3D Object Detection with Channel-wise Transformer

"Improving 3D Object Detection with Channel-wise Transformer" Thanks for the OpenPCDet, this implementation of the CT3D is mainly based on the pcdet v

Hualian Sheng 107 Dec 20, 2022
Code for Transformer Hawkes Process, ICML 2020.

Transformer Hawkes Process Source code for Transformer Hawkes Process (ICML 2020). Run the code Dependencies Python 3.7. Anaconda contains all the req

Simiao Zuo 111 Dec 26, 2022
Language-Driven Semantic Segmentation

Language-driven Semantic Segmentation (LSeg) The repo contains official PyTorch Implementation of paper Language-driven Semantic Segmentation. Authors

Intelligent Systems Lab Org 416 Jan 03, 2023
The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

OC-SORT Observation-Centric SORT (OC-SORT) is a pure motion-model-based multi-object tracker. It aims to improve tracking robustness in crowded scenes

Jinkun Cao 325 Jan 05, 2023
PyTorch - Python + Nim

Master Release Pytorch - Py + Nim A Nim frontend for pytorch, aiming to be mostly auto-generated and internally using ATen. Because Nim compiles to C+

Giovanni Petrantoni 425 Dec 22, 2022
Binary Passage Retriever (BPR) - an efficient passage retriever for open-domain question answering

BPR Binary Passage Retriever (BPR) is an efficient neural retrieval model for open-domain question answering. BPR integrates a learning-to-hash techni

Studio Ousia 147 Dec 07, 2022
Codes for TS-CAM: Token Semantic Coupled Attention Map for Weakly Supervised Object Localization.

TS-CAM: Token Semantic Coupled Attention Map for Weakly SupervisedObject Localization This is the official implementaion of paper TS-CAM: Token Semant

vasgaowei 112 Jan 02, 2023
Predictive AI layer for existing databases.

MindsDB is an open-source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning

MindsDB Inc 12.2k Jan 03, 2023
OpenMMLab 3D Human Parametric Model Toolbox and Benchmark

Introduction English | 简体中文 MMHuman3D is an open source PyTorch-based codebase for the use of 3D human parametric models in computer vision and comput

OpenMMLab 782 Jan 04, 2023
Code for A Volumetric Transformer for Accurate 3D Tumor Segmentation

VT-UNet This repo contains the supported pytorch code and configuration files to reproduce 3D medical image segmentaion results of VT-UNet. Environmen

Himashi Amanda Peiris 114 Dec 20, 2022
Deep Multimodal Neural Architecture Search

MMNas: Deep Multimodal Neural Architecture Search This repository corresponds to the PyTorch implementation of the MMnas for visual question answering

Vision and Language Group@ MIL 23 Dec 21, 2022
Code release for paper: The Boombox: Visual Reconstruction from Acoustic Vibrations

The Boombox: Visual Reconstruction from Acoustic Vibrations Boyuan Chen, Mia Chiquier, Hod Lipson, Carl Vondrick Columbia University Project Website |

Boyuan Chen 12 Nov 30, 2022
Code for Greedy Gradient Ensemble for Visual Question Answering (ICCV 2021, Oral)

Greedy Gradient Ensemble for De-biased VQA Code release for "Greedy Gradient Ensemble for Robust Visual Question Answering" (ICCV 2021, Oral). GGE can

21 Jun 29, 2022
SpecAugmentPyTorch - A Pytorch (support batch and channel) implementation of GoogleBrain's SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

SpecAugment An implementation of SpecAugment for Pytorch How to use Install pytorch, version=1.9.0 (new feature (torch.Tensor.take_along_dim) is used

IMLHF 3 Oct 11, 2022
This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark

SILG This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark. If you find this work helpful, please cons

Victor Zhong 17 Nov 27, 2022
This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR)

CEDR This repository is for Contrastive Embedding Distribution Refinement and Entropy-Aware Attention Network (CEDR) introduced in the following paper

phoenix 3 Feb 27, 2022
An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.

Neural Architecture Search with Random Labels(RLNAS) Introduction This project provides an implementation for Neural Architecture Search with Random L

18 Nov 08, 2022
Latex code for making neural networks diagrams

PlotNeuralNet Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, l

Haris Iqbal 18.6k Jan 01, 2023
Finite-temperature variational Monte Carlo calculation of uniform electron gas using neural canonical transformation.

CoulombGas This code implements the neural canonical transformation approach to the thermodynamic properties of uniform electron gas. Building on JAX,

FermiFlow 9 Mar 03, 2022