Home Backend Development Python Tutorial Discover Hidden Subdomains Effortlessly with SubDomainRadar.io and Python

Discover Hidden Subdomains Effortlessly with SubDomainRadar.io and Python

Sep 28, 2024 pm 04:10 PM

Discover Hidden Subdomains Effortlessly with SubDomainRadar.io and Python

As a cybersecurity professional, bug bounty hunter, or penetration tester, discovering hidden subdomains is critical for identifying potential vulnerabilities in a domain. Subdomains often host forgotten services or test environments that might be vulnerable to attacks.

In this post, I’ll introduce you to SubDomainRadar.io and its Python API wrapper — the ultimate tool for automating subdomain enumeration and reverse searches in your security workflows.

Why SubDomainRadar.io?

SubDomainRadar.io stands out because it uses over 40 private data sources to deliver a comprehensive subdomain discovery experience. Whether you need to run fast, deep, or reverse searches, this tool will help you find more subdomains than ever before.

Plus, with the SubDomainRadar Python library, you can easily integrate these powerful capabilities into your own projects and scripts.

Features of the Python Wrapper

With the SubDomainRadar Python API wrapper, you can:

  • Perform reverse searches on subdomains based on keywords
  • Enumerate domains with varying search depth (Fast, Medium, Deep)
  • Retrieve excluded domains and TLDs

Getting Started

To get started with the SubDomainRadar Python wrapper, you’ll need to install it via pip:

pip install subdomainradar
Copy after login

Once installed, you can start discovering subdomains with just a few lines of code!

Basic Usage Example

Here’s how to use the SubDomainRadar API to perform subdomain enumeration on a list of domains:

from subdomainradar import SubdomainRadarAPI

# Initialize the API client
api = SubdomainRadarAPI(base_url="https://api.subdomainradar.io", api_key="YOUR_API_KEY")

# Enumerate subdomains for a list of domains
domains = ["tesla.com", "google.com"]
results = api.enumerate_domains_with_results(domains=domains, group="Fast")

# Print the results
for domain, data in results.items():
    print(f"Domain: {domain}")
    print(f"Task ID: {data['task_id']}")
    print(f"Status: {data['status']}")
    print(f"Total Subdomains Found: {data['total_subdomains']}\n")

    print("Subdomains:")
    for subdomain_info in data.get('subdomains', []):
        print(f"  - Subdomain: {subdomain_info['subdomain']}")
        print(f"    IP: {subdomain_info.get('ip', '')}")
        print(f"    Country: {subdomain_info.get('country', 'No Country Info')}")
        print(f"    Reverse DNS: {subdomain_info.get('reverse_dns', [])}\n")

    print(f"WHOIS Information:")
    whois_info = data.get('whois', {})
    print(f"  Registrar: {whois_info.get('registrar', '')}")
    print(f"  Creation Date: {whois_info.get('creation_date', '')}")
    print(f"  Expiration Date: {whois_info.get('expiration_date', '')}")
    print(f"  Nameservers: {whois_info.get('nameservers', '')}\n")
Copy after login

In this example, we’re running a Fast enumeration search that quickly returns subdomains for the domains “tesla.com” and “google.com.” You can switch to Deep or Medium searches depending on how thorough you need to be.

Reverse Subdomain Search

One of the coolest features of SubDomainRadar.io is the ability to run reverse searches. If you’re looking for subdomains related to a specific keyword or part of a domain, this feature comes in handy.

Here’s how to run a reverse search using the Python wrapper:

# Perform a reverse search
results = api.reverse_search(subdomain_part="api", domain_part="car", tld_part="com")

for subdomain_info in results['subdomains']:
    subdomain = subdomain_info.get('subdomain', '')
    domain = subdomain_info.get('domain', '')
    tld = subdomain_info.get('tld', '')
    timestamp = subdomain_info.get('timestamp', '')

    if subdomain:
        complete_subdomain = f"{subdomain}.{domain}.{tld}"
    else:
        complete_subdomain = f"{domain}.{tld}"

    print(f"Complete Subdomain: {complete_subdomain}")
    print(f"Subdomain: {subdomain}")
    print(f"Domain: {domain}")
    print(f"TLD: {tld}")
    print(f"Timestamp: {timestamp}\n")
Copy after login

This allows you to find subdomains based on keywords or specific patterns, making it easier to locate targeted assets.

Try It Out Today!

If you’re looking for an effective, easy-to-use tool to discover all subdomains of a website, give SubDomainRadar.io a try. The SubDomainRadar Python wrapper makes it even easier to integrate subdomain discovery into your security workflow.

Ready to get started? Install the wrapper via pip and unlock the full power of SubDomainRadar.io today!

The above is the detailed content of Discover Hidden Subdomains Effortlessly with SubDomainRadar.io and Python. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Roblox: Bubble Gum Simulator Infinity - How To Get And Use Royal Keys
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Nordhold: Fusion System, Explained
1 months ago By 尊渡假赌尊渡假赌尊渡假赌
Mandragora: Whispers Of The Witch Tree - How To Unlock The Grappling Hook
4 weeks ago By 尊渡假赌尊渡假赌尊渡假赌
Clair Obscur: Expedition 33 - How To Get Perfect Chroma Catalysts
2 weeks ago By 尊渡假赌尊渡假赌尊渡假赌

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

Java Tutorial
1677
14
PHP Tutorial
1278
29
C# Tutorial
1257
24
Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Learning Python: Is 2 Hours of Daily Study Sufficient? Learning Python: Is 2 Hours of Daily Study Sufficient? Apr 18, 2025 am 12:22 AM

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Python vs. C  : Exploring Performance and Efficiency Python vs. C : Exploring Performance and Efficiency Apr 18, 2025 am 12:20 AM

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.

Python vs. C  : Understanding the Key Differences Python vs. C : Understanding the Key Differences Apr 21, 2025 am 12:18 AM

Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Which is part of the Python standard library: lists or arrays? Which is part of the Python standard library: lists or arrays? Apr 27, 2025 am 12:03 AM

Pythonlistsarepartofthestandardlibrary,whilearraysarenot.Listsarebuilt-in,versatile,andusedforstoringcollections,whereasarraysareprovidedbythearraymoduleandlesscommonlyusedduetolimitedfunctionality.

Python: Automation, Scripting, and Task Management Python: Automation, Scripting, and Task Management Apr 16, 2025 am 12:14 AM

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python for Scientific Computing: A Detailed Look Python for Scientific Computing: A Detailed Look Apr 19, 2025 am 12:15 AM

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Python for Web Development: Key Applications Python for Web Development: Key Applications Apr 18, 2025 am 12:20 AM

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

See all articles