Implementing Vulnerability Management With Greenbone

Implementing Vulnerability Management with Greenbone

Overview

Greenbone Vulnerability Management (GVM) is the open-source framework behind OpenVAS, providing comprehensive vulnerability scanning with over 100,000 Network Vulnerability Tests (NVTs). The python-gvm library provides a Python API to interact with GVM through the Greenbone Management Protocol (GMP), enabling programmatic creation of scan targets, task management, scan execution, and report retrieval. This skill covers connecting to GVM via Unix socket or TLS, authenticating, creating scan configs and targets, launching scans, and parsing XML-based vulnerability reports to produce actionable findings.

When to Use

Trigger phrases:

• "implementing vulnerability management with greenbone"

• "Deploy and operate Greenbone/OpenVAS vulnerability management using the python-g"

• When deploying or configuring implementing vulnerability management with greenbone capabilities in your environment

• When establishing security controls aligned to compliance requirements

• When building or improving security architecture for this domain

• When conducting security assessments that require this implementation

Prerequisites

• Greenbone Community Edition or Greenbone Enterprise Appliance installed
• Python 3.9+ with python-gvm (pip install python-gvm)
• GMP access credentials (username/password)
• Network connectivity to GVM daemon (Unix socket or TCP/TLS)
• Understanding of CVSS scoring and vulnerability classification

Steps

# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}

1. Install python-gvm: pip install python-gvm
2. Establish a GMP connection via UnixSocketConnection or TLSConnection
3. Authenticate with gmp.authenticate(username, password)
4. Create a target with gmp.create_target(name, hosts=[...], port_list_id=...)
5. Create a scan task with gmp.create_task(name, config_id, target_id, scanner_id)
6. Start the scan with gmp.start_task(task_id)
7. Monitor scan progress with gmp.get_task(task_id)
8. Retrieve results with gmp.get_report(report_id, report_format_id=...)
9. Parse the XML report for vulnerabilities, CVSS scores, and affected hosts
10. Generate a JSON summary report with severity distribution and remediation priorities

Expected Output

A JSON report containing total vulnerabilities found, severity breakdown (critical/high/medium/low), per-host findings with CVE references and CVSS scores, and scan metadata including duration and NVT feed version.

When NOT to Use

• You need to test the implementation (use performing-* skills)
• Task is about configuring existing tools (use configuring-* skills)
• You need to analyze security events (use analyzing-* skills)
• Task is about building detection rules (use building-* skills)
• You don't have access to the target environment
• Task requires vendor-specific expertise (consult vendor docs)

Red Flags

• Performing actions without explicit written authorization from the asset owner
• Testing against production systems without a defined scope and rules of engagement
• Sharing sensitive findings or credentials in unencrypted communications
• Failing to properly scope and contain the assessment before starting

Process

1. Reconnaissance — Gather target information, identify attack surface, enumerate services
2. Analysis/Exploitation — Execute the technique, analyze results, document findings
3. Reporting — Document IOCs, write findings, provide remediation recommendations

Verification

• All steps executed successfully against a test environment before production use
• Output documented with screenshots or logs demonstrating expected behavior
• Results validated against known-good baselines or reference implementations
• Documentation complete enough for another analyst to reproduce findings

Anti-Rationalization Table