Listen to this Post
During a recent interview, I was asked about my experience with CVE intelligence. I mentioned exploiting CVEs and modifying code but lacked tangible evidence to demonstrate my work, which is increasingly vital today. So, one thing I plan on building this week is a CVE search engine that aggregates CVEs from “all” public databases, incorporates my own findings, and identifies affected assets.
This is a random idea I had this morning, but it could be powerful if I had time to build it. The reason I am sharing this is because all pentesters should have their own version of this, there are no excuses! Connect xterm.js to run scans against infrastructure in real-time as well, basically anything. https://xtermjs.org/
You Should Know:
Building a CVE search engine requires a combination of web scraping, database management, and integration with security tools. Below are some practical steps, commands, and code snippets to help you get started:
1. Web Scraping for CVE Data
You can use Python with libraries like `BeautifulSoup` and `requests` to scrape CVE data from public databases like the NVD (National Vulnerability Database).
import requests
from bs4 import BeautifulSoup
url = "https://nvd.nist.gov/vuln/full-listing"
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
for link in soup.find_all('a', href=True):
if "CVE-" in link['href']:
print(link['href'])
2. Storing CVE Data
Use a database like SQLite or PostgreSQL to store the scraped CVE data.
<h1>Install SQLite</h1> sudo apt-get install sqlite3 <h1>Create a database and table</h1> sqlite3 cve_database.db CREATE TABLE cve_data (id INTEGER PRIMARY KEY, cve_id TEXT, description TEXT, published_date TEXT);
3. Integrating xterm.js for Real-Time Scanning
xterm.js can be integrated into a web interface to run real-time scans. Here’s a basic setup:
<h1>Clone the xterm.js repository</h1> git clone https://github.com/xtermjs/xterm.js.git cd xterm.js <h1>Install dependencies</h1> npm install <h1>Run the demo</h1> npm start
4. Running Vulnerability Scans
Use tools like `Nmap` or `OpenVAS` to scan for vulnerabilities on target systems.
<h1>Install Nmap</h1> sudo apt-get install nmap <h1>Run a basic scan</h1> nmap -sV -O target_ip
5. Automating CVE Matching
Write a script to match detected vulnerabilities with CVE data.
import sqlite3
def match_cve(software_version):
conn = sqlite3.connect('cve_database.db')
cursor = conn.cursor()
cursor.execute("SELECT * FROM cve_data WHERE description LIKE ?", (f'%{software_version}%',))
results = cursor.fetchall()
conn.close()
return results
What Undercode Say:
Building a CVE search engine is a powerful tool for penetration testers. By aggregating CVE data, integrating real-time scanning, and automating vulnerability matching, you can significantly enhance your pentesting workflow. Below are some additional Linux and Windows commands to further your understanding:
Linux Commands:
- Search for Installed Packages: `dpkg -l | grep package_name`
– Check Open Ports: `netstat -tuln`
– Update System: `sudo apt-get update && sudo apt-get upgrade`
Windows Commands:
- Check Installed Software: `wmic product get name,version`
– Scan for Open Ports: `netstat -an`
– Update System: `wuauclt /detectnow /updatenow`For more advanced CVE research, consider exploring the MITRE CVE List and CVE Details.
By combining these tools and techniques, you can create a robust CVE search engine tailored to your needs. Happy hacking!
References:
Reported By: Derek Lofaro – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
