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https://github.com/demining/fuzzing-bitcoin

Search for critical vulnerabilities and new methods of protecting cryptocurrency Bitcoin & Ethereum
https://github.com/demining/fuzzing-bitcoin

bitcoin btc cryptocurrency cryptography eth ethereum fuzzing smart-contracts vulnerabilities vulnerability

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Search for critical vulnerabilities and new methods of protecting cryptocurrency Bitcoin & Ethereum

Host: GitHub
URL: https://github.com/demining/fuzzing-bitcoin
Owner: demining
Created: 2024-06-04T21:14:07.000Z (7 months ago)
Default Branch: main
Last Pushed: 2024-06-04T21:23:33.000Z (7 months ago)
Last Synced: 2024-06-04T23:17:42.489Z (7 months ago)
Topics: bitcoin, btc, cryptocurrency, cryptography, eth, ethereum, fuzzing, smart-contracts, vulnerabilities, vulnerability
Language: Jupyter Notebook
Homepage: https://cryptodeeptech.ru/fuzzing-bitcoin
Size: 8.34 MB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

Fuzzing Bitcoin: Search for critical vulnerabilities and new methods of protecting cryptocurrency

Cryptographic applications and cryptocurrency wallets have critical components such as key generation, encryption/decryption, transaction signing, etc. These components should be the main targets for fuzz testing. Using fuzz testing in cryptographic applications and cryptocurrency wallets helps identify and fix vulnerabilities, increasing the security and reliability of the software.

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* Tutorial: https://youtu.be/CU4CFoxgKc8
* Tutorial: https://cryptodeeptech.ru/fuzzing-bitcoin
* Google Colab: https://colab.research.google.com/drive/1jxw_oBTd0HW6M2Mo_VDvdYcsXQyb6KHF

---

Fuzzing testing (or simply Fuzzing) is a software testing method that is used to identify vulnerabilities and errors by feeding random or specially generated data to a program’s input. In the context of cryptanalysis, fuzz testing is used to test cryptographic algorithms and systems for weaknesses that can be exploited by attackers.

The main idea of Fuzzing is to automatically generate a large amount of random or incorrect input data and feed it to the system under test. The system’s performance on this data is then analyzed to identify unexpected behaviors, failures, or vulnerabilities.

Fuzzing testing in cryptanalysis can help detect problems such as:

Buffer overflows: Errors that occur when writing data beyond the allocated memory.

Exception handling errors: Incorrect handling of unexpected or incorrect data.

Algorithm vulnerabilities: Weaknesses in the implementation of cryptographic algorithms that can be used for hacking.

This method is a powerful tool for securing cryptographic systems and helps developers create more reliable and secure software.

Applying fuzz testing to cryptocurrency wallets has several advantages:

Vulnerability detection: Fuzzing helps identify vulnerabilities that can be used by attackers to steal funds or compromise wallet security.

Increased reliability: Testing using random data helps identify errors that can lead to failures or incorrect operation of the wallet, which in turn increases its reliability.

Automate the testing process: Fuzzing testing can be automated, allowing you to run tests more frequently and efficiently than manual testing.

Diversity of test cases: Fuzzing generates a large variety of test cases, which helps identify errors that might otherwise go undetected using traditional testing methods.

Improved security: Regular fuzzing testing helps developers detect and fix vulnerabilities in a timely manner, which improves the overall security level of a cryptocurrency wallet.

Save time and resources: Automated fuzzing testing can save time and resources that would otherwise be spent on manual testing and debugging.

https://www.youtube.com/watch?v=CU4CFoxgKc8

Fuzzing can help detect the following types of vulnerabilities:

Input processing errors: Wallets may not correctly process input data such as addresses, transaction amounts, or keys. Fuzzing can reveal cases where incorrect data leads to crashes or incorrect operation of the wallet.

Buffer Overflow: If a Bitcoin wallet does not check the length of the input data, this can lead to a buffer overflow, which in turn can be used by attackers to execute arbitrary code.

Parser vulnerabilities: Wallets often use parsers to process transaction data and other inputs. Fuzzing can expose bugs in these parsers that could lead to crashes or security vulnerabilities.

Errors in cryptographic operations: Incorrect data processing in cryptographic operations can lead to leakage of private keys or other critical vulnerabilities. Fuzzing can help identify such errors.

API Vulnerabilities: If a Bitcoin wallet provides an API to communicate with other applications, fuzzing may reveal vulnerabilities in these interfaces that could be exploited to allow unauthorized access or unwanted operations.

Errors in transaction processing: Fuzzing can reveal errors in transaction processing logic that can lead to incorrect transactions or even loss of funds.

BitcoinChatGPT and choosing a fuzzing tool:

There are many tools for fuzzing testing, such as AFL (American Fuzzy Lop), libFuzzer, Honggfuzz and others. Fuzzing testing should be part of an ongoing development and testing process. Regular fuzzing tests will help to identify new vulnerabilities in a timely manner and maintain a high level of security for your cryptocurrency wallet. At the beginning of 2024, modern technologies that develop a pre-trained model Bitcoin ChatGPT and find effective ways to solve complex cryptographic problems that underlie the fuzzing testing method gained widespread popularity. Let’s consider an example of building the structure of a vulnerable Raw transaction that uses the BitcoinChatGPT module

BitcoinChatGPT #3 Fuzzing Vulnerability Algorithm

https://www.youtube.com/watch?v=ywauq3_WIs8

These tools can help identify vulnerabilities and improve the security of cryptocurrency wallets.

AFL (American Fuzzy Lop)

This is one of the most popular fuzz testing tools. It can be configured to test various types of software, including cryptocurrency wallets. American Fuzzy Lop (AFL) is a powerful fuzz testing tool that is commonly used to find vulnerabilities in software. Although AFL is primarily used via the command line, you can write a Python script to automate its launch. First, make sure you have AFL installed. If not, you can install it by following the instructions on the official AFL website.

https://www.youtube.com/watch?v=mhO5A9MlNuc

There are many useful videos on YouTube to help you better understand and use AFL. Here are some recommendations:

“American Fuzzy Lop (AFL) Tutorial” – This video usually explains the basics of using AFL, how to install it and get started with it.

“Fuzzing with AFL: A Practical Guide” – This video can offer a practical guide to fuzzing testing using AFL, including examples and demos.

“Advanced Fuzzing Techniques with AFL” – This video can cover more advanced techniques and strategies for using AFL effectively.

“AFL Fuzzing: Finding Bugs in Real-World Applications” – This video can show how to use AFL to find vulnerabilities in real-world applications, with examples and analysis.

“Setting Up AFL for Fuzz Testing” – This video can show you step by step how to set up AFL for Fuzz Testing on your system.

These videos will help you better understand how to use AFL to test the security of your software.

https://www.youtube.com/watch?v=5R2gPkCXZkM&list=PLHGgqcJIME5koI76OlBdBj7sCid1hbjf-

An example of a Python script that runs AFL:

#!/usr/bin/env python

import os

import subprocess

# Path to the AFL executable

afl_path = "/path/to/afl-fuzz"

# Path to the program under test

target_program = "/path/to/target_program"

# Path to the directory with input data

input_dir = "/path/to/input_dir"

# Path to the directory to save the output

output_dir = "/path/to/output_dir"

# Additional arguments for the program under test

target_args = "arg1 arg2"

# Forming a team to launch AFL

command = f"{afl_path} -i {input_dir} -o {output_dir} -- {target_program} {target_args}"

# Launch AFL

try:

 subprocess.run(command, shell=True, check=True)

 print("AFL started successfully.")

except subprocess.CalledProcessError as e:

 print(f"Error starting AFL: {e}")

In this script:

- afl_path — path to the AFL executable file (afl-fuzz).

- target_program — path to the program under test.

- input_dir — path to the directory with input data for fuzzing.

- output_dir — path to the directory to save the output data.

- target_args — additional arguments for the program under test.

This script generates a command to run AFL and executes it using subprocess.run. If AFL starts successfully, a corresponding message is displayed. If an error occurs, an error message is displayed.

Be sure to replace paths and arguments with appropriate values for your environment and program under test.

This script performs the following steps:

Defines the paths to the AFL, the target executable, the input directory, and the output directory.

Generates a command to launch AFL with the specified parameters.

Runs a command using
subprocess.run

libFuzzer

This is a fuzzing testing library that integrates with LLVM. It can be used to test C and C++ programs. LibFuzzer is a fuzz testing tool commonly used with C/C++ programs. However, you can use Python to automate the launch of LibFuzzer.

https://www.youtube.com/watch?v=3c43cvo6oBo

“Introduction to Fuzzing with libFuzzer” – This video provides a basic introduction to using libFuzzer for beginners.

“Fuzzing with libFuzzer and AddressSanitizer” – This video explains how to use libFuzzer with AddressSanitizer to detect vulnerabilities in your code.

“Advanced Fuzzing Techniques with libFuzzer” – This video is suitable for those who already know the basics and want to deepen their knowledge.

“Google Testing Blog: libFuzzer Tutorial” – A video tutorial from the Google team that covers various aspects of using libFuzzer.

“Fuzzing C/C++ Programs with libFuzzer” – This video covers specific examples and demonstrates the process of fuzzing C/C++programs.

These videos will help you better understand how to use libFuzzer to test and improve the security of your code.

https://www.youtube.com/watch?v=hFva8kJQwnc

Example Python code that runs LibFuzzer:

#!/usr/bin/env python

import subprocess

def run_libfuzzer(target_binary, corpus_dir, timeout=60):

 """

 Runs LibFuzzer on the specified binary in the given corpus directory.

 :param target_binary: Path to the binary file that will be fuzz tested.

 :param corpus_dir: Path to the corpus directory.

 :param timeout: Execution time in seconds (default 60 seconds).

 """

 try:

 # Command to run LibFuzzer

 command = [target_binary, corpus_dir, '-max_total_time={}'.format(timeout)]

 # Start process

 process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

 # Wait for process to complete

 stdout, stderr = process.communicate()

 # Output results

 print("LibFuzzer output:\n", stdout.decode())

 if stderr:

 print("LibFuzzer errors:\n", stderr.decode())

 except Exception as e:

 print(f"An error occurred while starting LibFuzzer: {e}")

# Usage example

target_binary = "./path/to/your/fuzz_target"

corpus_dir = "./path/to/your/corpus"

run_libfuzzer(target_binary, corpus_dir)

This script performs the following steps:

Defines a run_libfuzzer function that takes a binary path, a corpus directory, and an optional runtime.

Generates a command to launch LibFuzzer with the specified parameters.

Starts a process using subprocess.Popen and waits for it to complete.

Prints the execution results and any errors.

Make sure you have a LibFuzzer-enabled binary installed and compiled, and that you have a test data directory (corpus).

Hongfuzz

This is another powerful fuzz testing tool that supports various types of software and can be used to test cryptocurrency wallets. Honggfuzz is a powerful fuzz testing tool that can be run from Python using the subprocess module.

https://www.youtube.com/watch?v=keLfI65hHLg

“Fuzzing with Honggfuzz” – This video can give you a general idea of how to get started with Honggfuzz, including installation and basic commands.

“Advanced Fuzzing Techniques with Honggfuzz” – This video may cover more advanced techniques and settings for using Honggfuzz, which may be useful for more experienced users.

“Honggfuzz Tutorial for Beginners” – If you’re just starting out, this video could be a great place to start as it will likely cover the basic concepts and setup steps.

“Integrating Honggfuzz with CI/CD Pipelines” – This video can show how to integrate Honggfuzz into your continuous integration and delivery processes, which can be useful for test automation.

https://www.youtube.com/watch?v=6OBXJtEe-d8

Example code that demonstrates how to do this:

#!/usr/bin/env python

import subprocess

# Path to the Honggfuzz executable

honggfuzz_path = "/path/to/honggfuzz"

# Path to the application under test

target_app = "/path/to/target_app"

# Path to the directory with input data for fuzz testing

input_dir = "/path/to/input_dir"

# Path to the directory to save the results

output_dir = "/path/to/output_dir"

# Arguments for running Honggfuzz

args = [

 honggfuzz_path,

 "--input", input_dir,

 "--output", output_dir,

 "--", target_app

]

#LaunchHonggfuzz

try:

 result = subprocess.run(args, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)

 print("Honggfuzz started successfully!")

 print("Stdout output:", result.stdout.decode())

 print("Stderr output:", result.stderr.decode())

except subprocess.CalledProcessError as e:

 print("Error starting Honggfuzz!")

 print("Error code:", e.returncode)

 print("stdout output:", e.stdout.decode())

 print("Stderr output:", e.stderr.decode())

In this example:

honggfuzz_path– path to the Honggfuzz executable file.

target_app – path to the application being tested.

input_dir – path to the directory with input data for fuzz testing.

output_dir – path to the directory to save the results.

This script uses a module subprocessto run Honggfuzzwith the given arguments.

Be sure to replace the paths to honggfuzz, target_app, input_dirand output_dirwith the appropriate paths on your system.

OSS-Fuzz

This is a service from Google that provides an open source infrastructure for continuous fuzzing testing. It supports a variety of projects and can be configured to test cryptocurrency wallets. OSS-Fuzz helps you find bugs in open source software using fuzz testing. However, OSS-Fuzz is not run directly through Python code. Instead, you must configure your project to use OSS-Fuzz and then run it via the command line.

Let’s look at an example of how you can set up and run fuzz testing for your project using OSS-Fuzz.

https://www.youtube.com/watch?v=C8ZmJmzcYa4

“OSS-Fuzz: Continuous Fuzzing for Open Source Software” – This video from Google Open Source explains how OSS-Fuzz works and how it helps improve the security and stability of open source software.

“Fuzzing with OSS-Fuzz” – This video explains in detail how to get started using OSS-Fuzz for your project, including setup and integration.

“Google OSS-Fuzz: Continuous Fuzzing for Open Source Software” – Presentation from Google that covers the basic concepts and benefits of using OSS-Fuzz.

“Fuzzing 101: Getting Started with OSS-Fuzz” – A beginner’s tutorial that explains step-by-step how to get started with OSS-Fuzz.

“Integrating Your Project with OSS-Fuzz” – This video covers the practical aspects of integrating your project with OSS-Fuzz, including code examples and troubleshooting tips.

https://www.youtube.com/watch?v=7bvRbESPdlM

Here are the basic steps:

Setting up a project for OSS-Fuzz:

Create a Dockerfile for your project.

Write a build script for your project.

Launch OSS-Fuzz:

Use the command line to launch a Docker container and run fuzz tests.

Example Dockerfile and build script:

Dockerfile:

Dockerfile

FROM gcr.io/oss-fuzz-base/base-builder

RUN apt-get update && apt-get install -y make cmake

# Copy your project to the container

COPY. $SRC/your_project

# Go to the project directory

WORKDIR $SRC/your_project

# Run the build script

RUN ./build.sh

uild.sh:

bash

#!/bin/bash -eu

# Install the compiler for fuzz testing

export CC=clang

export CXX=clang++

# Create a build directory

mkdir build

cd build

# Run cmake and make to build the project

cmake..

make

# Compile the fuzz test

$CXX $CXXFLAGS -std=c++11 -I . \

 $SRC/your_project/fuzz_target.cpp -o $OUT/fuzz_target \

 $LIB_FUZZING_ENGINE

fuzz_target.cpp:

cpp

#include

#include

extern "C" int LLVMFuzzerTestOneInput(const uint8_t *data, size_t size) {

 // Your code for fuzz testing

 return 0;

}

Once you have configured the Dockerfile and build script, you can run OSS-Fuzz using the following commands:

sh # Build the Docker image docker build -t oss-fuzz/your_project .

# Run fuzz testing docker run --rm -v $PWD/out:/out oss-fuzz/your_project

These steps will help you set up and run fuzz testing for your project using OSS-Fuzz. If you have specific questions or need help setting up, please let me know!

Radamsa

This is a random data generator that can be used for fuzz testing. It is easy to use and can be integrated into various test scenarios. Radamsa is a fuzzing tool that can be useful for software testing.

https://www.youtube.com/watch?v=RPNvB3gwb0M

“Fuzzing with Radamsa” – This video explains how to use Radamsa for Fuzzing (testing software for vulnerabilities).

“Introduction to Fuzz Testing with Radamsa” – An introduction to Fuzz testing using Radamsa, including basic principles and examples.

“Radamsa: A Fuzzing Tool for Security Testing” – An overview of Radamsa’s capabilities and its application in the security field.

“How to Use Radamsa for Fuzz Testing” – Step-by-step guide on using Radamsa for Fuzz Testing.

https://www.youtube.com/watch?v=obY7YNvVWIs

To run Radamsa from Python, you can use the subprocess module for command line execution.

Example code:

Make sure Radamsa is installed on your computer. You can install it using
brew
on macOS or build from source on other systems.

Use the following Python code to run Radamsa:

#!/usr/bin/env python

import subprocess

def run_radamsa(input_file, output_file):

 try:

 # Run Radamsa specifying input and output files

 subprocess.run(['radamsa', input_file, '-o', output_file], check=True)

 print(f"Fuzzed data has been written to {output_file}")

 except subprocess.CalledProcessError as e:

 print(f"An error occurred while running Radamsa: {e}")

# Usage example

input_file = 'input.txt'

output_file = 'output.txt'

run_radamsa(input_file, output_file)

In this example:

-

input_file

is the path to the file you want to use as input to Radamsa.

-

output_file

— this is the path to the file in which the data generated by Radamsa will be written.

This code runs Radamsa with the specified input and output files and displays a shutdown or error message if one occurs. Make sure that the files input.txtand output.txtexist in the same directory as your script, or provide the full path to them.

Echidna

This is a tool for fuzz testing smart contracts in the Solidity language, which can be useful for testing wallets that interact with Ethereum.

https://www.youtube.com/watch?v=k5rA4Vh4Iew

“Echidna: Fuzzing for Ethereum Smart Contracts” – This video explains the basics of using Echidna to test smart contracts on Ethereum.

“Fuzzing Smart Contracts with Echidna” – This video takes a detailed look at the process of setting up and running Echidna for Fuzzing Smart Contracts.

“Echidna: A Fuzzer for Ethereum Smart Contracts” – This video discusses various aspects and capabilities of Echidna, as well as use cases.

“Smart Contract Security: Fuzzing with Echidna” – A video that focuses on smart contract security and using Echidna to find vulnerabilities.

https://www.youtube.com/watch?v=EA8_9x4D3Vk

To run Echidna using Python, you will need to use the shell command from Python.

Example code that shows how this can be done:

Make sure you have Echidna installed. You can install it by following the instructions on the official project page.

Use the subprocess module in Python to run Echidna.

#!/usr/bin/env python

import subprocess

# Path to your smart contract

contract_path = "path/to/your/contract.sol"

# Command to run Echidna

command = ["echidna-test", contract_path]

try:

 # Run command

 result = subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)

 # Output the result

 print("Echidna output:")

 print(result.stdout)

except subprocess.CalledProcessError as e:

 print("An error occurred while running Echidna:")

 print(e.stderr)

This script runs Echidna on the specified smart contract and outputs the result to the console. Make sure you replace path/to/your/contract.sol with the actual path to your smart contract.

Peach Fuzzer

A commercial fuzzing testing tool that supports multiple protocols and data formats. It can be used to test the security of cryptocurrency wallets. Peach Fuzzer is a popular fuzzing framework used to test the security and reliability of software by providing unexpected or random inputs.

https://www.youtube.com/watch?v=gROEZOxLVB8

“Peach Fuzzer Tutorial” – This video typically explains the basics of using Peach Fuzzer, including installation and configuration.

“Fuzzing with Peach: A Beginner’s Guide” – This video may be useful for those who are just getting started with Peach Fuzzer and want to understand the basic concepts and techniques.

“Advanced Peach Fuzzer Techniques” – This video covers more advanced aspects of using Peach Fuzzer, such as creating your own tests and analyzing the results.

“Peach Fuzzer in Action: Real-World Examples” – Here you can see how Peach Fuzzer is used to find vulnerabilities in real-world applications.

“Setting Up a Fuzzing Environment with Peach” – This video will help you set up your work environment to effectively use Peach Fuzzer.

https://www.youtube.com/watch?v=RvySx9B9RAg

Peach Fuzzer is not written in Python and typically requires configuration files and specific setup steps to run.

To run Peach Fuzzer, you typically need to create a Peach Pit XML file that defines the structure of the data you want to Fuzz and the target application. You then use the Peach command line tool to perform the Fuzzing process.

Here’s a basic example of how you can get Peach Fuzzer up and running using Python to invoke the command line tool. This example assumes that Peach Fuzzer is installed and properly configured on your system.

Create a Peach Fuzzer XML file (eg example.xml):

Python script to run Peach Fuzzer:

#!/usr/bin/env python

import subprocess

def run_peach_fuzzer(peach_pit_file):

    try:

        # Command to run Peach Fuzzer

        command = ['peach', peach_pit_file]

        

        # Execute the command

        result = subprocess.run(command, capture_output=True, text=True)

        

        # Print the output

        print("Peach Fuzzer Output:")

        print(result.stdout)

        

        # Check for errors

        if result.stderr:

            print("Peach Fuzzer Errors:")

            print(result.stderr)

    except Exception as e:

        print(f"An error occurred: {e}")

# Path to the Peach Pit XML file

peach_pit_file = 'example.xml'

# Run the Peach Fuzzer

run_peach_fuzzer(peach_pit_file)

This script uses the Python subprocess module to call the Peach Fuzzer command line tool with the specified Peach Pit XML file. 
Be sure to replace "example.xml" with the path to the actual Peach Fuzzer file.

Note: This example assumes that the peach command is available on PATHyour system. If this is not the case, you may need to provide the full path to the Peach executable. Also, make sure that you have the necessary permissions to run Peach Fuzzer and that all dependencies are installed correctly.

Conclusion:

Fuzzing is a powerful security testing method that can significantly improve the stability of cryptocurrency systems such as Bitcoin. The study identified potential vulnerabilities and weaknesses in Bitcoin software, highlighting the need for ongoing security monitoring and improvement. The use of fuzzing allows not only to detect errors in the early stages of development, but also to prevent possible attacks, which is especially important in the context of the growing popularity and importance of cryptocurrencies. In the future, integrating fuzzing into standard testing procedures could be a key step towards ensuring the reliability and security of decentralized financial systems.

References:

1. “Fuzzing for Security Testing: Theory and Practice” – review articles on methods and techniques of fuzzing.

2. “Bitcoin: A Peer-to-Peer Electronic Cash System” is an original article by Satoshi Nakamoto describing the basics of Bitcoin.

3. “Fuzzing: Art, Science, and Engineering” – articles describing various approaches to fuzzing and their application in security.

4. “Fuzzing for Software Security Testing and Quality Assurance” – a book dedicated to fuzzing methods and tools.

5. “Mastering Bitcoin: Unlocking Digital Cryptocurrencies” is a book that provides a deep understanding of how Bitcoin works.

6. “Looking for Lacunae in Bitcoin Core’s Fuzzing Efforts” Dynamic analysis: Software testing and debugging

7. “ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection” Bo Jiang, Ye Liu, and WK Chan. 2018. ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection

8. “Fuzzing Ethereum Smart Contracts” Roberto Ponte and Miguel Correia INESC-ID, Instituto Superior Tecnico, Universidade de Lisboa / Iberia Medeiros LaSIGE, Faculdade de Ciencias, Universidade de Lisboa.

9. “TokenAuditor: Detecting Manipulation Risk in Token Smart Contract by Fuzzing” 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS).

10. “Learning to Fuzz from Symbolic Execution” with Application to Smart Contracts.

11. “Finding Consensus Bugs in Ethereum via Multi-transaction Differential Fuzzing” Youngseok Yang, Seoul National University; Taesoo Kim, Georgia Institute of Technology; Byung-Gon Chun, Seoul National University and FriendlyAI

12. “The Human Side of Fuzzing: Challenges Faced by Developers” During Fuzzing Activities (Software testing and debugging; Empirical studies; Surveys and overviews).

https://dzen.ru/video/watch/665f6986a2886608ad194e31

This material was created for the CRYPTO DEEP TECH portal to ensure financial security of data and elliptic curve cryptography secp256k1 against weak ECDSA signatures in the BITCOIN cryptocurrency . The creators of the software are not responsible for the use of materials.

Source

Google Colab

BitcoinChatGPT

Telegram: https://t.me/cryptodeeptech

Video material: https://youtu.be/CU4CFoxgKc8

Dzen Video Tutorial: https://dzen.ru/video/watch/665f6986a2886608ad194e31

Source: https://cryptodeeptech.ru/fuzzing-bitcoin

Fuzzing Bitcoin: Search for critical vulnerabilities and new methods of protecting cryptocurrency