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

Last update: Dec 13, 2022

Overview

TargetAllDomainObjects

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

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.

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

1 Dec 25, 2021

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

3 Oct 31, 2022

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,

32 Dec 13, 2022

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

0 Nov 17, 2022

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

202 Dec 13, 2022

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].

44 Dec 7, 2022

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

18 Nov 21, 2022

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

21 Jul 30, 2022

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

2 Aug 22, 2022

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

Related tags

Overview

TargetAllDomainObjects

Features

Usage

Demo

Contributing

You might also like...

A Pytorch Implementation of [Source data‐free domain adaptation of object detector through domain

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

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

Protect against subdomain takeover

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

Defending graph neural networks against adversarial attacks (NeurIPS 2020)

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

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

Cards Against Humanity AI

Releases(1.1)

1.1(Feb 16, 2022)

Owner

Podalirius

Veri Setinizi Yolov5 Formatına Dönüştürün

RetinaNet-PyTorch - A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection

TyXe: Pyro-based BNNs for Pytorch users

Minimal But Practical Image Classifier Pipline Using Pytorch, Finetune on ResNet18, Got 99% Accuracy on Own Small Datasets.

TANL: Structured Prediction as Translation between Augmented Natural Languages

Language-Agnostic Website Embedding and Classification

One Million Scenes for Autonomous Driving

I-SECRET: Importance-guided fundus image enhancement via semi-supervised contrastive constraining

Full body anonymization - Realistic Full-Body Anonymization with Surface-Guided GANs

[TPDS'21] COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

🔥 Cogitare - A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python

A multilingual version of MS MARCO passage ranking dataset

Build a medical knowledge graph based on Unified Language Medical System (UMLS)

Single Image Super-Resolution (SISR) with SRResNet, EDSR and SRGAN

A code repository associated with the paper A Benchmark for Rough Sketch Cleanup by Chuan Yan, David Vanderhaeghe, and Yotam Gingold from SIGGRAPH Asia 2020.

Code for our ICASSP 2021 paper: SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

Extreme Dynamic Classifier Chains - XGBoost for Multi-label Classification

TEDSummary is a speech summary corpus. It includes TED talks subtitle (Document), Title-Detail (Summary), speaker name (Meta info), MP4 URL, and utterance id

pytorch implementation of the ICCV'21 paper "MVTN: Multi-View Transformation Network for 3D Shape Recognition"

CellRank's reproducibility repository.