A Python-based security tool designed to automatically fetch, filter, analyze, and match public exploits to the software installed on a user’s system. This project provides real-time exploit monitoring, similarity-based exploit–software matching, and automated email alerts.
Example usage:
python App.py -d 2023-05-21 -w-d: Specify the date for exploits (default: today)-w,-hw,-m: Specify the type of exploit to extract (Windows, Hardware, Multiple)
- 📅 Fetch exploits by date from online sources (including .onion links)
- 🛡️ Filter exploits by type: Windows, Win32, Hardware, Multiple
- 🤖 Apply fuzzy string matching (
token_set_ratio) to map exploit titles to installed software - 🎯 Adjustable similarity threshold to optimize detection accuracy
- ✉️ Automated email alerting for relevant vulnerabilities
- 🖥️ PyQT interface for visualization of vulnerability alerts and system status
- ⚡ Lightweight, real-time monitoring and data processing
| Category | Technologies |
|---|---|
| Programming | Python |
| Web Scraping | Mechanize, BeautifulSoup |
| Fuzzy Matching | FuzzyWuzzy |
| GUI | PyQt5 |
| Database | SQLite (internal logging) |
-
Fetching Exploits by Date Exploits are retrieved via a date-specific URL:
http://mvfjfugdwgc5uwho.onion/date/If no-dis provided, today’s date is used automatically. -
Filtering by Type Users can filter exploits by type using CLI flags:
-w→ Windows / Win32-hw→ Hardware-m→ Multi-platform exploits
-
Matching Exploits to Installed Software Fuzzy string matching (
token_set_ratio) compares exploit titles to local software names, resolving noise from version numbers or extra wording. A threshold of 75% provides reliable matching. -
Automated Email Alerts Relevant matches trigger an email notification using Python’s
emailandsmtplib. Duplicate notifications are automatically prevented. -
Visualization A PyQt-based GUI displays matched exploits, alerts, history, and system status.
Exploit Title:
WinRAR 5.61 - Path Traversal Exploit
Installed Software:
WinRAR 5.70 (64-bit)
Even though the version differs, token_set_ratio detects high similarity (e.g., 76/100), signaling a relevant vulnerability.
Mohammad Alaei AI Researcher & Computer Engineer 🔗 https://alaeimo.ir
This project combines cybersecurity automation, intelligent similarity matching, and real-time alerting, reflecting practical experience in exploit analysis, system monitoring, and security-focused software engineering.