A universal memory dumper using Frida

Related tags

Deep Learningfridump
Overview

Fridump

Fridump (v0.1) is an open source memory dumping tool, primarily aimed to penetration testers and developers. Fridump is using the Frida framework to dump accessible memory addresses from any platform supported. It can be used from a Windows, Linux or Mac OS X system to dump the memory of an iOS, Android or Windows application.

Usage

How to:

  fridump [-h] [-o dir] [-U] [-v] [-r] [-s] [--max-size bytes] process

The following are the main flags that can be used with fridump:

  positional arguments:
  process            the process that you will be injecting to

  optional arguments:
  -h, --help         show this help message and exit
  -o dir, --out dir  provide full output directory path. (def: 'dump')
  -U, --usb          device connected over usb
  -v, --verbose      verbose
  -r, --read-only    dump read-only parts of memory. More data, more errors
  -s, --strings      run strings on all dump files. Saved in output dir.
  --max-size bytes   maximum size of dump file in bytes (def: 20971520)

To find the name of a local process, you can use:

  frida-ps

For a process that is running on a USB connected device, you can use:

  frida-ps -U

Examples:

  fridump -U Safari   -   Dump the memory of an iOS device associated with the Safari app
  fridump -U -s com.example.WebApp   -  Dump the memory of an Android device and run strings on all dump files
  fridump -r -o [full_path]  -  Dump the memory of a local application and save it to the specified directory

More examples can be found here

Installation

To install Fridump you just need to clone it from git and run it:

  git clone https://github.com/Nightbringer21/fridump.git
        
  python fridump.py -h

Pre-requisites

To use fridump you need to have frida installed on your python environment and frida-server on the device you are trying to dump the memory from. The easiest way to install frida on your python is using pip:

pip install frida

More information on how to install Frida can be found here

For iOS, installation instructions can be found here.

For Android, installation instructions can be found here.

Note: On Android devices, make sure that the frida-server binary is running as root!

Disclaimer

  • This is version 0.1 of the software, so I expect some bugs to be present
  • I am not a developer, so my coding skills might not be the best

This tool has been tested on a Windows 7 and a Mac OS X laptop, dumping the memory of:

  • an iPad Air 2 running iOS 8.2
  • a Galaxy Tab running Cyanogenmod 4.4.4
  • a Windows 7 laptop.

Therefore, if this tool is not working for you, I apologise and I will try to fix it.

Any suggestions and comments are welcome!

A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/15

Pradyumna Reddy Chinthala 190 Dec 15, 2022
A bunch of random PyTorch models using PyTorch's C++ frontend

PyTorch Deep Learning Models using the C++ frontend Gettting started Clone the repo 1. https://github.com/mrdvince/pytorchcpp 2. cd fashionmnist or

Vince 0 Jul 13, 2021
PyTorch wrapper for Taichi data-oriented class

Stannum PyTorch wrapper for Taichi data-oriented class PRs are welcomed, please see TODOs. Usage from stannum import Tin import torch data_oriented =

86 Dec 23, 2022
Bayesian algorithm execution (BAX)

Bayesian Algorithm Execution (BAX) Code for the paper: Bayesian Algorithm Execution: Estimating Computable Properties of Black-box Functions Using Mut

Willie Neiswanger 38 Dec 08, 2022
Fast image augmentation library and an easy-to-use wrapper around other libraries

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

11.4k Jan 09, 2023
This repository collects project-relevant Isabelle/HOL formalizations.

Isabelle/HOL formalizations related to the AuReLeE project Formalization of Abstract Argumentation Frameworks See AbstractArgumentation folder for the

AuReLeE project 1 Sep 10, 2022
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank

This repository provides the official code for replicating experiments from the paper: Semi-Supervised Semantic Segmentation with Pixel-Level Contrast

Iñigo Alonso Ruiz 58 Dec 15, 2022
Reinforcement Learning for the Blackjack

Reinforcement Learning for Blackjack Author: ZHA Mengyue Math Department of HKUST Problem Statement We study playing Blackjack by reinforcement learni

Dolores 3 Jan 24, 2022
Pre-trained NFNets with 99% of the accuracy of the official paper

NFNet Pytorch Implementation This repo contains pretrained NFNet models F0-F6 with high ImageNet accuracy from the paper High-Performance Large-Scale

Benjamin Schmidt 133 Dec 09, 2022
Pytorch implementation of the paper SPICE: Semantic Pseudo-labeling for Image Clustering

SPICE: Semantic Pseudo-labeling for Image Clustering By Chuang Niu and Ge Wang This is a Pytorch implementation of the paper. (In updating) SOTA on 5

Chuang Niu 154 Dec 15, 2022
A hybrid SOTA solution of LiDAR panoptic segmentation with C++ implementations of point cloud clustering algorithms. ICCV21, Workshop on Traditional Computer Vision in the Age of Deep Learning

ICCVW21-TradiCV-Survey-of-LiDAR-Cluster Motivation In contrast to popular end-to-end deep learning LiDAR panoptic segmentation solutions, we propose a

YimingZhao 103 Nov 22, 2022
Open source annotation tool for machine learning practitioners.

doccano doccano is an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequ

7.1k Jan 01, 2023
Memory Efficient Attention (O(sqrt(n)) for Jax and PyTorch

Memory Efficient Attention This is unofficial implementation of Self-attention Does Not Need O(n^2) Memory for Jax and PyTorch. Implementation is almo

Amin Rezaei 126 Dec 27, 2022
Codes for NeurIPS 2021 paper "Adversarial Neuron Pruning Purifies Backdoored Deep Models"

Adversarial Neuron Pruning Purifies Backdoored Deep Models Code for NeurIPS 2021 "Adversarial Neuron Pruning Purifies Backdoored Deep Models" by Dongx

Dongxian Wu 31 Dec 11, 2022
robomimic: A Modular Framework for Robot Learning from Demonstration

robomimic [Homepage]   [Documentation]   [Study Paper]   [Study Website]   [ARISE Initiative] Latest Updates [08/09/2021] v0.1.0: Initial code and pap

ARISE Initiative 178 Jan 05, 2023
Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation

OSCAR Project Page | Paper This repository contains the codebase used in OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Ma

NVIDIA Research Projects 74 Dec 22, 2022
A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

Note: This is an alpha (preview) version which is still under refining. nn-Meter is a novel and efficient system to accurately predict the inference l

Microsoft 244 Jan 06, 2023
Existing Literature about Machine Unlearning

Machine Unlearning Papers 2021 Brophy and Lowd. Machine Unlearning for Random Forests. In ICML 2021. Bourtoule et al. Machine Unlearning. In IEEE Symp

Jonathan Brophy 213 Jan 08, 2023
EfficientDet (Scalable and Efficient Object Detection) implementation in Keras and Tensorflow

EfficientDet This is an implementation of EfficientDet for object detection on Keras and Tensorflow. The project is based on the official implementati

1.3k Dec 19, 2022
PyTorch implementation of Convolutional Neural Fabrics http://arxiv.org/abs/1606.02492

PyTorch implementation of Convolutional Neural Fabrics arxiv:1606.02492 There are some minor differences: The raw image is first convolved, to obtain

Anuvabh Dutt 25 Dec 22, 2021