dyld_shared_cache processing / Single-Image loading for BinaryNinja

Last update: Dec 28, 2022

Related tags

Overview

Dyld Shared Cache Parser

Author: cynder (kat)

Dyld Shared Cache Support for BinaryNinja

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

Open the plugin manager
Search for "Dyld" and install this plugin

Usage:

Open Dyld Shared Cache file with BN
Select the Image you would like to disassemble
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.

《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

51 Jun 24, 2022

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

149 Jan 8, 2023

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

50 Dec 27, 2022

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

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.

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

71 Dec 30, 2022

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

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

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

140 Nov 23, 2022

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

12 Dec 19, 2022

Comments

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

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

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)

1.0.0(Mar 1, 2022)

Initial Release
Source code(tar.gz)
Source code(zip)

Owner

cynder

macOS/iOS development @ reverse engineering chick. // maintainer of the iPhone Dev Wiki (https://iphonedev.wiki)

GitHub Repository

A convolutional recurrent neural network for classifying A/B phases in EEG signals recorded for sleep analysis.

CAP-Classification-CRNN A deep learning model based on Inception modules paired with gated recurrent units (GRU) for the classification of CAP phases

2 Nov 25, 2022

text_recognition_toolbox: The reimplementation of a series of classical scene text recognition papers with Pytorch in a uniform way.

text recognition toolbox 1. 项目介绍该项目是基于pytorch深度学习框架，以统一的改写方式实现了以下6篇经典的文字识别论文，论文的详情如下。该项目会持续进行更新，欢迎大家提出问题以及对代码进行贡献。模型论文标题发表年份模型方法划分 CRNN 《An End-t

168 Dec 24, 2022

A user-friendly research and development tool built to standardize RL competency assessment for custom agents and environments.

Built with ❤️ by Sam Showalter Contents Overview Installation Dependencies Usage Scripts Standard Execution Environment Development Environment Benchm

1 Nov 18, 2021

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset

YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics open-source research int

73 Dec 16, 2022

Extreme Lightwegith Portrait Segmentation

Extreme Lightwegith Portrait Segmentation Please go to this link to download code Requirements python 3 pytorch = 0.4.1 torchvision==0.2.1 opencv-pyt

59 Dec 16, 2022

PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluation of Visual Stories via Semantic Consistency"

Improving Generation and Evaluation of Visual Stories via Semantic Consistency PyTorch code for the NAACL 2021 paper "Improving Generation and Evaluat

28 Dec 08, 2022

DziriBERT: a Pre-trained Language Model for the Algerian Dialect

DziriBERT DziriBERT is the first Transformer-based Language Model that has been pre-trained specifically for the Algerian Dialect. It handles Algerian

117 Jan 07, 2023

Python Algorithm Interview Book Review

파이썬 알고리즘 인터뷰 책 리뷰 리뷰 IT 대기업에 들어가고 싶은 목표가 있다. 내가 꿈꿔온 회사에서 일하는 사람들의 모습을 보면 멋있다고 생각이 들고 나의 목표에 대한 열망이 강해지는 것 같다. 미래의 핵심 사업 중 하나인 SW 부분을 이끌고 발전시키는 우리나라의 I

1 Dec 14, 2021

CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

CharacterGAN Implementation of the paper "CharacterGAN: Few-Shot Keypoint Character Animation and Reposing" by Tobias Hinz, Matthew Fisher, Oliver Wan

181 Dec 27, 2022

Model serving at scale

Run inference at scale Cortex is an open source platform for large-scale machine learning inference workloads. Workloads Realtime APIs - respond to pr

7.9k Jan 06, 2023

Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch

SRDenseNet-pytorch Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch (http://openaccess.thecvf.com/content_ICC

114 Nov 26, 2022

This is an official implementation of "Polarized Self-Attention: Towards High-quality Pixel-wise Regression"

Polarized Self-Attention: Towards High-quality Pixel-wise Regression This is an official implementation of: Huajun Liu, Fuqiang Liu, Xinyi Fan and Don

212 Jan 08, 2023

Time-stretch audio clips quickly with PyTorch (CUDA supported)! Additional utilities for searching efficient transformations are included.

22 Jul 07, 2022

Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization'

pytorch-AdaIN This is an unofficial pytorch implementation of a paper, Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization [Hua

873 Jan 06, 2023

Sdf sparse conv - Deep Learning on SDF for Classifying Brain Biomarkers

Deep Learning on SDF for Classifying Brain Biomarkers To reproduce the results f

1 Jan 25, 2022

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model The task of age transformation illustrates the change of an individual

444 Dec 30, 2022

Winning Solution in NTIRE19 Challenges on Video Restoration and Enhancement (CVPR19 Workshops) - Video Restoration with Enhanced Deformable Convolutional Networks. EDVR has been merged into BasicSR and this repo is a mirror of BasicSR.

EDVR has been merged into BasicSR. This GitHub repo is a mirror of BasicSR. Recommend to use BasicSR, and open issues, pull requests, etc in BasicSR.

1.4k Dec 25, 2022

Orbivator AI - To Determine which features of data (measurements) are most important for diagnosing breast cancer and find out if breast cancer occurs or not.

Orbivator_AI Breast Cancer Wisconsin (Diagnostic) GOAL To Determine which features of data (measurements) are most important for diagnosing breast can

1 Jan 02, 2022

This code is for eCaReNet: explainable Cancer Relapse Prediction Network.

eCaReNet This code is for eCaReNet: explainable Cancer Relapse Prediction Network. (Towards Explainable End-to-End Prostate Cancer Relapse Prediction

2 Jul 28, 2022

A model that attempts to learn and benefit from data collected on card counting.

A model that attempts to learn and benefit from data collected on card counting. A decision tree like model is built to win more often than loose and increase the bet of the player appropriately to c

1 Dec 17, 2021