Piotr - IoT firmware emulation instrumentation for training and research

Related tags

Deep Learningpiotr
Overview

Piotr: Pythonic IoT exploitation and Research

Introduction to Piotr

Piotr is an emulation helper for Qemu that provides a convenient way to create, share and run virtual IoT devices. It only supports the ARM Architecture at the moment.

Piotr is heavily inspired from @therealsaumil's ARM-X framework and keeps the same approach: emulated devices run inside an emulated host that provides all the tools you may need and creates a fake environment for them. This approach allows remote debugging with gdbserver or fridaserver, provides a steady platform for vulnerability research, exploitation and training.

Moreover, Piotr is able to package any emulated device into a single file that may be shared and imported by other users, thus sharing its kernel, DTB file or even its host filesystem. This way, it is possible to create new emulated devices based upon existing ones, and to improve all of them by simply changing a single file (kernel, host filesystem, etc.).

How does Piotr work ?

Piotr stores everything it needs inside a specific user directory called .piotr, located in the user's home directory. This directory stores all the kernels, dtb files, host filesystems and emulated devices.

Each emulated device is stored in a specific subdirectory of your .piotr/devices directory, and must contain at least:

  • a config.yaml file containing the device's qemu configuration in a readable way
  • a root filesystem with correct permissions and groups and users

When Piotr is asked to emulate a specific device, it loads its config.yaml file, parses it and creates a Qemu emulated device with the corresponding specifications.

This emulated device can then be driven by Piotr's helper tools in order to:

  • list or kill running processes
  • dynamically configure network interfaces
  • debug any process running on the emulated device
  • ...

Reference documentation

Piotr's reference documentation is available on Read The Docs. If you want to start using Piotr as soon as possible, we recommend you to read our Quickstart guide !

License

Piotr is released under the MIT license, see LICENSE for more information.

Owner
Damien Cauquil
Proud dad, happy geek, random hacker.
Damien Cauquil
Kaggleship: Kaggle Notebooks

Kaggleship: Kaggle Notebooks This repository contains my Kaggle notebooks. They are generally about data science, machine learning, and deep learning.

Erfan Sobhaei 1 Jan 25, 2022
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Super Resolution Examples We run this script under TensorFlow 2.0 and the TensorLayer2.0+. For TensorLayer 1.4 version, please check release. 🚀 🚀 🚀

TensorLayer Community 2.9k Jan 08, 2023
A Python module for parallel optimization of expensive black-box functions

blackbox: A Python module for parallel optimization of expensive black-box functions What is this? A minimalistic and easy-to-use Python module that e

Paul Knysh 426 Dec 08, 2022
3D mesh stylization driven by a text input in PyTorch

Text2Mesh [Project Page] Text2Mesh is a method for text-driven stylization of a 3D mesh, as described in "Text2Mesh: Text-Driven Neural Stylization fo

Threedle (University of Chicago) 649 Dec 27, 2022
Self-Guided Contrastive Learning for BERT Sentence Representations

Self-Guided Contrastive Learning for BERT Sentence Representations This repository is dedicated for releasing the implementation of the models utilize

Taeuk Kim 16 Dec 04, 2022
Processed, version controlled history of Minecraft's generated data and assets

mcmeta Processed, version controlled history of Minecraft's generated data and assets Repository structure Each of the following branches has a commit

Misode 75 Dec 28, 2022
The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021)

The Official PyTorch Implementation of "LSGM: Score-based Generative Modeling in Latent Space" (NeurIPS 2021) Arash Vahdat*   ·   Karsten Kreis*   ·  

NVIDIA Research Projects 238 Jan 02, 2023
👐OpenHands : Making Sign Language Recognition Accessible (WiP 🚧👷‍♂️🏗)

👐 OpenHands: Sign Language Recognition Library Making Sign Language Recognition Accessible Check the documentation on how to use the library: ReadThe

AI4Bhārat 69 Dec 12, 2022
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation

This is the official implementation of our paper: Bowen Wen, Wenzhao Lian, Kostas Bekris, and Stefan Schaal. "CaTGrasp: Learning Category-Level Task-R

Bowen Wen 199 Jan 04, 2023
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

News December 27: v1.1.0 New loss functions: CentroidTripletLoss and VICRegLoss Mean reciprocal rank + per-class accuracies See the release notes Than

Kevin Musgrave 5k Jan 05, 2023
Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

HV-plane reconstruction from a single 360 image Code for our paper in CVPR 2021: Indoor Panorama Planar 3D Reconstruction via Divide and Conquer (pape

sunset 36 Jan 03, 2023
Log4j JNDI inj. vuln scanner

Log-4-JAM - Log 4 Just Another Mess Log4j JNDI inj. vuln scanner Requirements pip3 install requests_toolbelt Usage # make sure target list has http/ht

Ashish Kunwar 66 Nov 09, 2022
FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection

FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection FCOSR: A Simple Anchor-free Rotated Detector for Aerial Object Detection arXi

59 Nov 29, 2022
Search Youtube Video and Get Video info

PyYouTube Get Video Data from YouTube link Installation pip install PyYouTube How to use it ? Get Videos Data from pyyoutube import Data yt = Data("ht

lokaman chendekar 35 Nov 25, 2022
Tutorial materials for Part of NSU Intro to Deep Learning with PyTorch.

Intro to Deep Learning Materials are part of North South University (NSU) Intro to Deep Learning with PyTorch workshop series. (Slides) Related materi

Hasib Zunair 9 Jun 08, 2022
The code for the CVPR 2021 paper Neural Deformation Graphs, a novel approach for globally-consistent deformation tracking and 3D reconstruction of non-rigid objects.

Neural Deformation Graphs Project Page | Paper | Video Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction Aljaž Božič, Pablo P

Aljaz Bozic 134 Dec 16, 2022
SynNet - synthetic tree generation using neural networks

SynNet This repo contains the code and analysis scripts for our amortized approach to synthetic tree generation using neural networks. Our model can s

Wenhao Gao 60 Dec 29, 2022
Udacity's CS101: Intro to Computer Science - Building a Search Engine

Udacity's CS101: Intro to Computer Science - Building a Search Engine All soluti

Phillip 0 Feb 26, 2022
Implementation of 'lightweight' GAN, proposed in ICLR 2021, in Pytorch. High resolution image generations that can be trained within a day or two

512x512 flowers after 12 hours of training, 1 gpu 256x256 flowers after 12 hours of training, 1 gpu Pizza 'Lightweight' GAN Implementation of 'lightwe

Phil Wang 1.5k Jan 02, 2023
TRACER: Extreme Attention Guided Salient Object Tracing Network implementation in PyTorch

TRACER: Extreme Attention Guided Salient Object Tracing Network This paper was accepted at AAAI 2022 SA poster session. Datasets All datasets are avai

Karel 118 Dec 29, 2022