Skip to content

Sahethi/CrossBrowserExtension

Repository files navigation

🛡️ CBX: A Cross Browser Extension to Detect Multiple Web Attacks

License Paper

CBX is a comprehensive security-focused browser extension designed to detect and mitigate common web-based attacks in real-time. Built to be compatible across Chromium-based browsers, it empowers users and organizations with proactive protection against web vulnerabilities like XSS, CSRF, SQLi, and more.


📌 Features

  • 🔐 Detects multiple web vulnerabilities:
    • Cross-Site Scripting (XSS)
    • Cross-Site Request Forgery (CSRF)
    • SQL Injection (SQLi)
    • Tabnabbing
    • Phishing
    • Frame Busting (iFrame-based attacks)
  • 🧠 ML-based phishing detection (trained on PhishTank dataset)
  • 🌍 Cross-browser compatibility: Chrome, Edge, Opera, Brave
  • ⚡ Lightweight with minimal performance overhead
  • 🔄 Real-time detection and alerts

📚 Research Backing

This extension is backed by academic research and was published at IEEE:

📄 A Survey Paper on Browser Extensions to Detect Web Attack
IEEE Xplore


🚀 Getting Started

1. Clone the Repository

git clone https://github.com/your-username/CBX-Browser-Extension.git
cd CBX-Browser-Extension

2. Load the Extension

To load the extension in your browser:

For Chrome, Brave, or Edge:

  1. Open your browser and go to chrome://extensions/
  2. Enable Developer mode (toggle in the top-right corner)
  3. Click Load unpacked
  4. Browse and select the root directory of this project (where manifest.json is located)

System Architecture

Architecture Screenshot

📸 Screenshots

XSS

Popup Screenshot Popup Screenshot

iFrame

iFrame Alert

Tabnabbing

Tabnabbing

Phishing, CSRF, SQLi

Phishing, CSRF, SQLi


🛠️ Technologies Used

  • HTML5, CSS3, JavaScript (Vanilla)
  • Chrome Extension APIs (Manifest v3)
  • Machine Learning (Phishing Detection)
  • Static + Behavioral Analysis
  • Browser DOM & Event Listeners
  • Secure Local Storage
  • VirusTotal API (for Phishing analysis)

📈 Results & Evaluation

CBX was tested against simulated environments and real phishing datasets. Detection accuracy highlights:

  • XSS Detection: ~93%
  • SQL Injection: ~91%
  • Phishing Detection (ML-based): ~96%
  • Tabnabbing & Clickjacking: Rule-based detection with >90% efficacy

Details are available in the full report.


🧪 Testing Tools Used

  • Ghost Inspector: For automated UI testing
  • Selenium IDE: For interaction flow testing
  • LambdaTest: Cross-browser testing automation

🧩 Limitations

  • Some false positives in edge cases (especially with aggressive input sanitization)
  • Resource usage may spike on content-heavy pages
  • Not compatible with all manifest versions (tested on Manifest v3)
  • Currently supports Chromium-based browsers only

📅 Future Scope

  • Threat intelligence feed integration (VirusTotal, URLScan)
  • Enhanced UI and centralized dashboard
  • Custom rule configuration per organization

👨‍💻 Contributors

  • Sahethi Depuru Guru
  • Ayush Pattnaik
  • Rutuja Kolte
  • Nikhil Sharma

Under the guidance of Prof. Varshapriya J. N., VJTI Mumbai


📄 License

This project is licensed under the MIT License.
See LICENSE.md for details.

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors