Automating Vulnerability Detection at Scale

Introduction

At Recorded Future, one of our key challenges is staying ahead of the rapidly evolving threat landscape. With thousands of new vulnerabilities disclosed each year, manual analysis and rule creation simply doesn't scale. This post explores how we built automation tools to enhance our vulnerability detection capabilities.

The Challenge

Traditional vulnerability management faces several challenges:

• Volume: Hundreds of new CVEs are published weekly
• Speed: Time-to-detection is critical for zero-day threats
• Accuracy: False positives waste valuable analyst time
• Coverage: Ensuring comprehensive detection across diverse technologies

Our Approach

We developed a multi-layered automation system that combines:

1. Automated CVE Ingestion

import requests
import json
from datetime import datetime, timedelta

class CVEIngester:
    def __init__(self):
        self.nvd_api = "https://services.nvd.nist.gov/rest/json/cves/2.0"
    
    def fetch_recent_cves(self, days=7):
        """Fetch CVEs from the last N days"""
        end_date = datetime.now()
        start_date = end_date - timedelta(days=days)
        
        params = {
            'pubStartDate': start_date.isoformat(),
            'pubEndDate': end_date.isoformat()
        }
        
        response = requests.get(self.nvd_api, params=params)
        return response.json()