How do you automate vulnerability scoring in the CVSS system

Question

I am working on a security assessment project and want to automate vulnerability scoring using the Common Vulnerability Scoring System (CVSS). I need help with:

• Extracting vulnerability data from sources like NVD or MITRE.
• Automating CVSS score calculation based on attack vectors and impact metrics.
• Generating reports with structured vulnerability rankings.
  If anyone has experience with Python libraries or APIs that facilitate CVSS automation, I'd appreciate some guidance.

Answer 1

Automating vulnerability scoring using the Common Vulnerability Scoring System (CVSS) involves several key steps: extracting vulnerability data from reputable sources, calculating CVSS scores based on defined metrics, and generating structured reports for analysis. Here's a structured approach to achieve this:

1. Extracting Vulnerability Data

To obtain up-to-date vulnerability information, you can utilize the National Vulnerability Database (NVD) APIs:

• NVD APIs: The NVD offers a comprehensive API that allows retrieval of vulnerability data in JSON format. You can query the database for specific CVEs or a collection of vulnerabilities based on various parameters. Detailed documentation is available at the NVD's API documentation page.

  Example of fetching data for a specific CVE:

  import requests

  cve_id = 'CVE-2023-12345'
  url = f'https://services.nvd.nist.gov/rest/json/cves/2.0?cveId={cve_id}'
  response = requests.get(url)
  data = response.json()

2. Automating CVSS Score Calculation

Once you have the vulnerability data, calculate the CVSS scores using Python libraries:

• cvss Library: This Python package supports CVSS v2, v3, and v4 calculations. It provides utilities to compute scores and includes an interactive calculator. You can install it via pip:

pip install cvss

Example of calculating a CVSS v3 score:

  from cvss import CVSS3

  vector = 'CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H'
  cvss = CVSS3(vector)
  print(f'Base Score: {cvss.base_score}')
  print(f'Severity: {cvss.severity}')

This will output the base score and severity based on the provided vector.

3. Generating Structured Vulnerability Reports

After calculating the scores, organize the data into structured reports:

• Using Pandas for Data Handling: The pandas library is excellent for handling and exporting data:

pip install pandas

Example of creating a DataFrame and exporting to CSV:

  import pandas as pd

  data = {
      'CVE_ID': ['CVE-2023-12345'],
      'Base_Score': [cvss.base_score],
      'Severity': [cvss.severity],
      'Vector': [vector]
  }
  df = pd.DataFrame(data)
  df.to_csv('vulnerability_report.csv', index=False)

This script creates a CSV file with the CVE ID, base score, severity, and vector.

4. Integrating with Continuous Monitoring Systems

For continuous assessment, integrate the automation script into your security infrastructure:

• Scheduled Scripts: Use cron jobs (on Unix-like systems) or Task Scheduler (on Windows) to run your script at regular intervals, ensuring your vulnerability data and scores are up-to-date.

• Alerting Mechanisms: Enhance your script to send alerts (e.g., emails or messages) when high-severity vulnerabilities are detected, enabling prompt action.

By following these steps, you can automate the process of extracting vulnerability data, calculating CVSS scores, and generating structured reports, thereby enhancing your security assessment capabilities.