Ethersplay: Visual Disassembler for EVM Bytecode Built on Binary Ninja

ethersplay

Binary Ninja plugin which enables an EVM disassembler and related analysis tools.

Installation

Ethersplay only supports Python >= 3.6.

Ensure that your Binary Ninja's Python library is set to Python 3.6+. You can change the ScriptingProvider in the Advanced Settings.

Install the dependencies:

$ pip install -r requirements.txt

Create a symbolic link to the Binary Ninja plugin folder. E.g., in macOS

cd ~/Library/Application\ Support/Binary\ Ninja/plugins
ln -s <your_download_location>/ethersplay/ethersplay .

Ubuntu:

cd ~/.binaryninja/plugins
ln -s <your_download_location>/ethersplay/ethersplay .

Demo

Example

How to Use

Ethersplay takes as input the evm bytecode in raw binary format.

To have the bytecode of a solidity file, use solc:

  • solc --bin-runtime file.sol: to print the bytecode of the runtime part of the contract (for most of the cases).
  • solc --bin file.sol: to print the initialisation bytecode of the contract (constructor),

Example using test.sol with following contents:

contract Test {
    uint256 value;
    function Test() {
        value = 5;
    }
    function set_value(uint256 v) {
        value = v;
    }
    function() payable {}
}

Run solidity to compile: solc --bin-runtime test.sol

solc prints the bytecode to stdout in the format below:

======= test.sol:Test =======
Binary of the runtime part:
60606040523615603d576000357c0100000000000000000000000000000000000000000000000000000000900463ffffffff168063b0f2b72a146041575b5b5b005b3415604b57600080fd5b605f60048080359060200190919050506061565b005b806000819055505b505600a165627a7a72305820c177a64bf54a26574918ddc2201f7ab2dd8619d6c3ee87ce9aaa1eb0e0b1d4650029

Copy the ascii hex string, and then create a new file in Binary Ninja. Right-click and select Paste From -> Raw Hex. The output should look identical to the earlier example image. Save this file as test.evm and close it. Alternatively, paste the ascii hex string into a new text file, and run the utils/convert_bytecode.py on that file.

test.evm can now be loaded into Binary Ninja.

Note: The file must end in .evm to be recognized as an EVM bytecode binary file.

Plugins

Render Flowgraphs

Generates a clean control flow graph of all functions.

Before: before

After: !after

Manticore coverage

Colors the basic blocks explored through Manticore (using the visited.txt or *.trace files).

Download details:
Author: crytic
Source code: https://github.com/crytic/ethersplay
License: AGPL-3.0 license

#solidity #smartcontract #blockchain #ethereum #python

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Ethersplay: Visual Disassembler for EVM Bytecode Built on Binary Ninja
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Binary MLM Software Demo | Binary Compensation Plan, MLM Woocommerce

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Ethersplay: Visual Disassembler for EVM Bytecode Built on Binary Ninja

ethersplay

Binary Ninja plugin which enables an EVM disassembler and related analysis tools.

Installation

Ethersplay only supports Python >= 3.6.

Ensure that your Binary Ninja's Python library is set to Python 3.6+. You can change the ScriptingProvider in the Advanced Settings.

Install the dependencies:

$ pip install -r requirements.txt

Create a symbolic link to the Binary Ninja plugin folder. E.g., in macOS

cd ~/Library/Application\ Support/Binary\ Ninja/plugins
ln -s <your_download_location>/ethersplay/ethersplay .

Ubuntu:

cd ~/.binaryninja/plugins
ln -s <your_download_location>/ethersplay/ethersplay .

Demo

Example

How to Use

Ethersplay takes as input the evm bytecode in raw binary format.

To have the bytecode of a solidity file, use solc:

  • solc --bin-runtime file.sol: to print the bytecode of the runtime part of the contract (for most of the cases).
  • solc --bin file.sol: to print the initialisation bytecode of the contract (constructor),

Example using test.sol with following contents:

contract Test {
    uint256 value;
    function Test() {
        value = 5;
    }
    function set_value(uint256 v) {
        value = v;
    }
    function() payable {}
}

Run solidity to compile: solc --bin-runtime test.sol

solc prints the bytecode to stdout in the format below:

======= test.sol:Test =======
Binary of the runtime part:
60606040523615603d576000357c0100000000000000000000000000000000000000000000000000000000900463ffffffff168063b0f2b72a146041575b5b5b005b3415604b57600080fd5b605f60048080359060200190919050506061565b005b806000819055505b505600a165627a7a72305820c177a64bf54a26574918ddc2201f7ab2dd8619d6c3ee87ce9aaa1eb0e0b1d4650029

Copy the ascii hex string, and then create a new file in Binary Ninja. Right-click and select Paste From -> Raw Hex. The output should look identical to the earlier example image. Save this file as test.evm and close it. Alternatively, paste the ascii hex string into a new text file, and run the utils/convert_bytecode.py on that file.

test.evm can now be loaded into Binary Ninja.

Note: The file must end in .evm to be recognized as an EVM bytecode binary file.

Plugins

Render Flowgraphs

Generates a clean control flow graph of all functions.

Before: before

After: !after

Manticore coverage

Colors the basic blocks explored through Manticore (using the visited.txt or *.trace files).

Download details:
Author: crytic
Source code: https://github.com/crytic/ethersplay
License: AGPL-3.0 license

#solidity #smartcontract #blockchain #ethereum #python

Arvel  Parker

Arvel Parker

1591177440

Visual Analytics and Advanced Data Visualization

Visual Analytics is the scientific visualization to emerge an idea to present data in such a way so that it could be easily determined by anyone.

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The human brain is fast and is built to process things faster. So Data visualization provides its way to make things easy for students, researchers, mathematicians, scientists e

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Arvel  Parker

Arvel Parker

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Visual Analytics Services for Data-Driven Decision Making

Visual analytics is the process of collecting, examining complex and large data sets (structured or unstructured) to get useful information to draw conclusions about the datasets and visualize the data or information in the form of interactive visual interfaces and graphical manner.

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As human brain process visual content better than it processes plain text. So using advanced visual interfaces, humans may directly interact with the data analysis capabilities of today’s computers and allow them to make well-informed decisions in complex situations.

It allows you to create beautiful, interactive dashboards or reports that are immediately available on the web or a mobile device. The tool has a Data Explorer that makes it easy for the novice analyst to create forecasts, decision trees, or other fancy statistical methods.

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Brain Crist

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A visualization library for Python built on these principles using matplotlib

Visualizations should tell a story, and tell it in a beautiful way. Multiplex is a visualization library for Python built on these principles using matplotlib. This library aims to make it as easy as possible for you to transform data into beautiful visualizations that tell a story.

The instructions in this README.md file will get you a copy of the project up and running.

For use-cases of Multiplex, check out the Jupyter Notebook examples.

To read more about Multiplex, read the documentation.

Who is Multiplex for?

Multiplex is aimed at data scientists, researchers, students and all those who work with data and are familiar with Python.

Why should I use Multiplex?

If Multiplex is based on matplotlib, why not use matplotlib directly?

Multiplex does not replace matplotlib.

Anything that you can do with Multiplex, you can also do with matplotlib.

Multiplex makes it easier to create beautiful visualizations.

This is achieved by providing:

  • 4 custom matplotlib styles;
  • Functionality to caption visualizations;
  • Functionality to annotate any visualization with text; and
  • New types of visualizations not available in matplotlib:
  • 100% bar chart,
  • Network graph, and
  • Text-only visualizations.

Multiplex automatically lays out your data so that you can focus on telling your story.

How do I use Multiplex?

Prerequisites

Multiplex is based on matplotlib.

You can install matplotlib using pip: python -m pip install -U matplotlib.

More details about it are available in matplotlib’s repository.

Multiplex also uses the following libraries in certain visualizations:

Installing

You can install Multiplex using pip: python -m pip install -U multiplex-plot.

Quickstart

Creating visualizations with Multiplex is very easy.

For example, you can create a text-only visualization in just 10 lines of code, including all styling options:

import matplotlib.pyplot as plt
from multiplex import drawable
plt.style.use('styles/multiplex.style')
viz = drawable.Drawable(plt.figure(figsize=(10, 2)))
paragraph = """Anthony Lopes is a Portuguese professional footballer who plays for Olympique Lyonnais as a goalkeeper. He came through the youth ranks at Lyon, being called to the first team in 2011 and making his debut the following year."""
style = { 'align': 'justify', 'fontfamily': 'serif', 'alpha': 0.9, 'lineheight': 1.25, 'lpad': 0.1, 'rpad': 0.1 }
viz.draw_text_annotation(paragraph, **style)
viz.set_title('Profile: Anthony Lopes', loc='left')
viz.set_caption("""Wikipedia is a useful repository to get more information about anything. Below is an excerpt from the Wikipedia profile of footballer Anthony Lopes.""")
viz.show()

Python

Example text annotation

All it takes to draw a simple text visualization is 10 lines of code:

  1. 3 lines to import matplotlib, Multiplex and the visualization style;
  2. 3 lines to set up the visualization object, load the data and set the style;
  3. 4 lines to draw and show the visualization, including a title and caption.

Multiplex does all the tedious work for you: the layout, alignment and more.

At the same time, you can take as much control as you want.

Using Multiplex is very easy, but you can get started by checking out the Jupyter Notebook tutorials for an easy-to-follow tour of Multiplex’s capabilities.

Example visualizations

Example bar chart

Example time series

Example network graph

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