dyld_shared_cache processing / Single-Image loading for BinaryNinja

Related tags

Deep Learningbn-dyldsharedcache
Overview

Dyld Shared Cache Parser

Author: cynder (kat)

Dyld Shared Cache Support for BinaryNinja

BinaryNinja Screenshot

BinaryNinja Screenshot

Without any of the fuss of requiring manually loading several unrelated images, or the awful off-image addresses, and with better output than IDA, Hopper, or any other disassembler on the market.

Installation + Usage

  1. Open the plugin manager
  2. Search for "Dyld" and install this plugin

Usage:

  1. Open Dyld Shared Cache file with BN
  2. Select the Image you would like to disassemble
  3. Congrats, you are now Reverse Engineering the Mach-O

Description:

This project acts as an interface for two seperate projects; DyldExtractor, and ktool. Mainly DyldExtractor.

DyldExtractor is a project written primarily by 'arandomdev' designed for CLI standalone dyld_shared_cache extraction. It is the best tool for the job, and reverses the majority of "optimizations" that make DSC reverse engineering ugly and painful. Utilizing this plugin, Binja's processing should outperform IDAs, and wont require IDA's need for repeatedly right clicking and manually loading tons of modules.

This version of DyldExtractor has a lot of modifications (read: a lot of commented out lines) from the original designed to make it function better in the binja environment.

ktool is a multifaceted project I wrote for, primarily, MachO + ObjC Parsing.

It is mainly used for super basic parsing of the output, as we need to properly write the segments to the VM (and scrap all the dsc data that was originally in this file) so the Mach-O View knows how to parse it.

License

This plugin, along with ktool and dyldextractor are released under an MIT license. Both of these plugins are vendored within this project to make installation slightly simpler.

You might also like...
《Single Image Reflection Removal Beyond Linearity》(CVPR 2019)

Single-Image-Reflection-Removal-Beyond-Linearity Paper Single Image Reflection Removal Beyond Linearity. Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, G

Qiang Wen 51 Jun 24, 2022
Official PyTorch code of Holistic 3D Scene Understanding from a Single Image with Implicit Representation (CVPR 2021)
Official PyTorch code of Holistic 3D Scene Understanding from a Single Image with Implicit Representation (CVPR 2021)

Implicit3DUnderstanding (Im3D) [Project Page] Holistic 3D Scene Understanding from a Single Image with Implicit Representation Cheng Zhang, Zhaopeng C

Cheng Zhang 149 Jan 8, 2023
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image
Learning to Reconstruct 3D Manhattan Wireframes from a Single Image

Learning to Reconstruct 3D Manhattan Wireframes From a Single Image This repository contains the PyTorch implementation of the paper: Yichao Zhou, Hao

Yichao Zhou 50 Dec 27, 2022
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation (RA-L/ICRA 2020)
Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation (RA-L/ICRA 2020)

Aerial Depth Completion This work is described in the letter "Aerial Single-View Depth Completion with Image-Guided Uncertainty Estimation", by Lucas

ETHZ V4RL 70 Dec 22, 2022
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.
This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard evaluation metric to measure the accuracy and robustness of 3D face reconstruction methods from a single image under variations in viewing angle, lighting, and common occlusions.

NoW Evaluation This is the official repository for evaluation on the NoW Benchmark Dataset. The goal of the NoW benchmark is to introduce a standard e

Soubhik Sanyal 71 Dec 30, 2022
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image
Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

NonCuboidRoom Paper Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image Cheng Yang*, Jia Zheng*, Xili Dai, Rui Tang, Yi Ma, Xiao

null 67 Dec 15, 2022
Selective Wavelet Attention Learning for Single Image Deraining

SWAL Code for Paper "Selective Wavelet Attention Learning for Single Image Deraining" Prerequisites Python 3 PyTorch Models We provide the models trai

Bobo 9 Jun 17, 2022
PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network"

HAN PyTorch code for our ECCV 2020 paper "Single Image Super-Resolution via a Holistic Attention Network" This repository is for HAN introduced in the

五维空间 140 Nov 23, 2022
Code for generating a single image pretraining dataset
Code for generating a single image pretraining dataset

Single Image Pretraining of Visual Representations As shown in the paper A critical analysis of self-supervision, or what we can learn from a single i

Yuki M. Asano 12 Dec 19, 2022
Comments
  • TypeError: cannot unpack non-iterable NoneType object

    TypeError: cannot unpack non-iterable NoneType object

    Tried this just now, and got this, trying to extract the macOS 13.1 x86_64h cache:

    Successfully installed: Dyld Shared Cache Processor
Loaded python3 plugin 'cxnder_bndyldsharedcache'
Traceback (most recent call last):
  File "/Applications/Binary Ninja.app/Contents/MacOS/plugins/../../Resources/python/binaryninja/binaryview.py", line 2818, in _init
    return self.init()
  File "/Users/torarne/Library/Application Support/Binary Ninja/repositories/community/plugins/cxnder_bndyldsharedcache/dsc.py", line 101, in init
    stub_fixer.fixStubs(extraction_ctx)
  File "/Users/torarne/Library/Application Support/Binary Ninja/repositories/community/plugins/cxnder_bndyldsharedcache/DyldExtractor/converter/stub_fixer.py", line 1681, in fixStubs
    _StubFixer(extractionCtx).run()
  File "/Users/torarne/Library/Application Support/Binary Ninja/repositories/community/plugins/cxnder_bndyldsharedcache/DyldExtractor/converter/stub_fixer.py", line 1011, in run
    self._symbolizer = _Symbolizer(self._extractionCtx)
  File "/Users/torarne/Library/Application Support/Binary Ninja/repositories/community/plugins/cxnder_bndyldsharedcache/DyldExtractor/converter/stub_fixer.py", line 59, in __init__
    self._enumerateExports()
  File "/Users/torarne/Library/Application Support/Binary Ninja/repositories/community/plugins/cxnder_bndyldsharedcache/DyldExtractor/converter/stub_fixer.py", line 101, in _enumerateExports
    if depInfo := self._getDepInfo(dylib, self._machoCtx):
  File "/Users/torarne/Library/Application Support/Binary Ninja/repositories/community/plugins/cxnder_bndyldsharedcache/DyldExtractor/converter/stub_fixer.py", line 179, in _getDepInfo
    imageOff, dyldCtx = self._dyldCtx.convertAddr(imageAddr)
TypeError: cannot unpack non-iterable NoneType object
BinaryView of type 'DyldSharedCache' failed to initialize!
No available/valid debug info parsers for `Raw` view
Found more than 'analysis.limits.stringSearch' (0x100000) strings aborting search for range: 0 - 0x33be0000
Analysis update took 12.239 seconds
    opened by torarnv 1
  • prep for plugin manager

    prep for plugin manager

    Looks like only two changes are required to get this added to the BN plugin manager. The first is to add a requirements.txt -- while ktool and DyldExtractor are versioned, capstone is still a requirement of DyldExtractor so it would be nice to expose that.

    Or, better yet, replace the disassembler with BN's own disassembly to remove the dependency entirely. That also means there's no need to hack around the lack of PAC instructions as BN can disassemble those just fine.

    The other step is to make a release, then we can add the plugin directly to the plugin manager which would be really handy!

    opened by psifertex 1
  • fix relative imports for built-in BN Py 3.8.9 on MacOS

    fix relative imports for built-in BN Py 3.8.9 on MacOS

    I'm not sure whether it's the exact python version or the fact that I'm using the BN shipped Python versus homebrew / ports but I'm unable to use the plugin as-is on MacOS without this change. I don't know how much this versioned DyldExtractor has differed, happy to test/submit upstream in the parent repo if you prefer.

    opened by psifertex 0
Releases(1.0.0)
Owner
cynder
macOS/iOS development @ reverse engineering chick. // maintainer of the iPhone Dev Wiki (https://iphonedev.wiki)
cynder
GitHub Repository
Dictionary Learning with Uniform Sparse Representations for Anomaly Detection

Dictionary Learning with Uniform Sparse Representations for Anomaly Detection Implementation of the Uniform DL Representation for AD algorithm describ

Paul Irofti 1 Nov 23, 2022
Get a Grip! - A robotic system for remote clinical environments.

Get a Grip! Within clinical environments, sterilization is an essential procedure for disinfecting surgical and medical instruments. For our engineeri

Jay Sharma 1 Jan 05, 2022
An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and Machine Learning.

ALgorithmic_Trading_with_ML An algorithmic trading bot that learns and adapts to new data and evolving markets using Financial Python Programming and

1 Mar 14, 2022
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Hiring research interns for visual transformer

Multimedia Research 484 Dec 29, 2022
Implementation for HFGI: High-Fidelity GAN Inversion for Image Attribute Editing

HFGI: High-Fidelity GAN Inversion for Image Attribute Editing High-Fidelity GAN Inversion for Image Attribute Editing Update: We released the inferenc

Tengfei Wang 371 Dec 30, 2022
Pixel Consensus Voting for Panoptic Segmentation (CVPR 2020)

Implementation for Pixel Consensus Voting (CVPR 2020). This codebase contains the essential ingredients of PCV, including various spatial discretizati

Haochen 23 Oct 25, 2022
Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness

Imbalanced Gradients: A Subtle Cause of Overestimated Adversarial Robustness Code for Paper "Imbalanced Gradients: A Subtle Cause of Overestimated Adv

Hanxun Huang 11 Nov 30, 2022
[WACV 2020] Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints

Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints Official implementation for Reducing Footskate in Human Motion Recon

Virginia Tech Vision and Learning Lab 38 Nov 01, 2022
CT-Net: Channel Tensorization Network for Video Classification

[ICLR2021] CT-Net: Channel Tensorization Network for Video Classification @inproceedings{ li2021ctnet, title={{\{}CT{\}}-Net: Channel Tensorization Ne

33 Nov 15, 2022
EsViT: Efficient self-supervised Vision Transformers

Efficient Self-Supervised Vision Transformers (EsViT) PyTorch implementation for EsViT, built with two techniques: A multi-stage Transformer architect

Microsoft 352 Dec 25, 2022
Joint-task Self-supervised Learning for Temporal Correspondence (NeurIPS 2019)

Joint-task Self-supervised Learning for Temporal Correspondence Project | Paper Overview Joint-task Self-supervised Learning for Temporal Corresponden

Sifei Liu 167 Dec 14, 2022
A simple Python library for stochastic graphical ecological models

What is Viridicle? Viridicle is a library for simulating stochastic graphical ecological models. It implements the continuous time models described in

Theorem Engine 0 Dec 04, 2021
3rd Place Solution for ICCV 2021 Workshop SSLAD Track 3A - Continual Learning Classification Challenge

Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay 3rd Place Solution for ICCV 2021 Workshop SS

Rifki Kurniawan 6 Nov 10, 2022
PyTorch implementation of HDN(Homography Decomposition Networks) for planar object tracking

Homography Decomposition Networks for Planar Object Tracking This project is the offical PyTorch implementation of HDN(Homography Decomposition Networ

CaptainHook 48 Dec 15, 2022
Reinforcement learning algorithms in RLlib

raylab Reinforcement learning algorithms in RLlib and PyTorch. Installation pip install raylab Quickstart Raylab provides agents and environments to b

Ângelo 50 Sep 08, 2022
2021 CCF BDCI 全国信息检索挑战杯（CCIR-Cup）智能人机交互自然语言理解赛道第二名参赛解决方案

2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup) 智能人机交互自然语言理解赛道第二名解决方案 比赛网址: CCIR-Cup-智能人机交互自然语言理解 1.依赖环境： python==3.8 torch==1.7.1+cu110 numpy==1.19.2 transformers=

JinXiang 22 Oct 29, 2022
Website which uses Deep Learning to generate horror stories.

Creepypasta - Text Generator Website which uses Deep Learning to generate horror stories. View Demo · View Website Repo · Report Bug · Request Feature

Dhairya Sharma 5 Oct 14, 2022
You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

You can draw the corresponding bounding box into the image and save it according to the result file (txt format) run by the tracker.

Huiyiqianli 42 Dec 06, 2022
Random Walk Graph Neural Networks

Random Walk Graph Neural Networks This repository is the official implementation of Random Walk Graph Neural Networks. Requirements Code is written in

Giannis Nikolentzos 38 Jan 02, 2023
Instant-nerf-pytorch - NeRF trained SUPER FAST in pytorch

instant-nerf-pytorch This is WORK IN PROGRESS, please feel free to contribute vi

94 Nov 22, 2022