implementing-rapid7-insightvm-for-scanning▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Deploy and configure Rapid7 InsightVM Security Console and Scan Engines for authenticated and unauthenticated vulnerability scanning across enterprise environments.
| name | implementing-rapid7-insightvm-for-scanning |
| description | Deploy and configure Rapid7 InsightVM Security Console and Scan Engines for authenticated and unauthenticated vulnerability scanning across enterprise environments. |
| domain | cybersecurity |
| subdomain | vulnerability-management |
| tags | - rapid7 - insightvm - vulnerability-scanning - nexpose - scan-engine - asset-discovery - authenticated-scanning |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - ID.RA-01 - ID.RA-02 - ID.IM-02 - ID.RA-06 |
Implementing Rapid7 InsightVM for Scanning
Overview
Rapid7 InsightVM (formerly Nexpose) is an enterprise vulnerability management platform that combines on-premises scanning via Security Console and Scan Engines with cloud-based analytics through the Insight Platform. InsightVM leverages Rapid7's vulnerability research library, Metasploit exploit knowledge, global attacker behavior data, internet-wide scanning telemetry, and real-time reporting to provide comprehensive vulnerability visibility. This skill covers deploying the Security Console, configuring Scan Engines, setting up scan templates, credentialed scanning, and integrating with the Insight Agent for continuous assessment.
When to Use
- When deploying or configuring implementing rapid7 insightvm for scanning 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
- Server meeting minimum requirements: 16 GB RAM, 4 CPU cores, 500 GB disk (Security Console)
- Scan Engine: 8 GB RAM, 4 CPU cores, 100 GB disk
- Network access to target subnets (ports vary by scan type)
- Administrative credentials for authenticated scanning (SSH, WMI, SNMP)
- Rapid7 InsightVM license and Insight Platform account
- PostgreSQL database (bundled with Security Console)
Core Concepts
InsightVM Architecture Components
Security Console
The central management server that:
- Hosts the web-based management interface (default port 3780)
- Stores scan results in an embedded PostgreSQL database
- Manages Scan Engine deployments and scan schedules
- Generates reports and dashboards
- Connects to Rapid7 Insight Platform for cloud analytics
Note: Security Console is NOT supported in containerized environments.
Scan Engines
Distributed scanning components that:
- Perform active network scanning against target assets
- Can be deployed across network segments for segmented environments
- Available as container images on Docker Hub for flexible deployment
- Report results back to the Security Console
Insight Agent
Lightweight endpoint agent providing:
- Continuous vulnerability assessment without network scans
- Assessment of remote/roaming endpoints
- Complement to engine-based scanning for comprehensive coverage
- Real-time asset inventory updates
Scan Template Types
| Template | Use Case | Depth |
|---|---|---|
| Discovery Scan | Asset inventory, host enumeration | Low |
| Full Audit without Web Spider | Standard vulnerability assessment | Medium |
| Full Audit Enhanced Logging | Deep assessment with verbose logging | High |
| HIPAA Compliance | Healthcare regulatory compliance | High |
| PCI ASV Audit | PCI DSS external scanning requirement | High |
| CIS Policy Compliance | Configuration benchmarking | Medium |
| Web Spider | Web application discovery and assessment | Medium |
Workflow
Step 1: Install Security Console
# Download InsightVM installer (Linux)
chmod +x Rapid7Setup-Linux64.bin
./Rapid7Setup-Linux64.bin -c
# Verify service is running
systemctl status nexposeconsole.service
# Access web interface
# https://<console-ip>:3780
Initial configuration:
- Navigate to https://localhost:3780
- Complete the setup wizard with license key
- Configure database settings (embedded PostgreSQL recommended)
- Set administrator credentials
- Activate Insight Platform connection for cloud analytics
Step 2: Deploy Distributed Scan Engines
# Install Scan Engine on remote server
./Rapid7Setup-Linux64.bin -c
# During installation, select "Scan Engine only"
# Pair with Security Console using shared secret
# Docker-based Scan Engine deployment
docker pull rapid7/insightvm-scan-engine
docker run -d \
--name scan-engine \
-p 40814:40814 \
-e CONSOLE_HOST=<console-ip> \
-e CONSOLE_PORT=3780 \
-e ENGINE_NAME=DMZ-Scanner \
-e SHARED_SECRET=<pairing-secret> \
rapid7/insightvm-scan-engine
Pair engines in Security Console:
- Administration > Scan Engines > New Scan Engine
- Enter engine hostname/IP and port (default 40814)
- Use shared secret for authentication
- Verify connectivity status shows "Active"
Step 3: Configure Asset Discovery Sites
Site Configuration:
Name: Production-Network
Scan Engine: Primary-Engine-01
Scan Template: Full Audit without Web Spider
Included Assets:
- 10.0.0.0/8 (Internal network)
- 172.16.0.0/12 (DMZ network)
Excluded Assets:
- 10.0.0.1 (Core router - fragile)
- 10.0.100.0/24 (ICS/SCADA segment)
Schedule:
Frequency: Weekly
Day: Sunday
Time: 02:00 AM
Max Duration: 8 hours
Step 4: Configure Authenticated Scanning
Windows Credentials (WMI)
Credential Type: Microsoft Windows/Samba (SMB/CIFS)
Domain: CORP.EXAMPLE.COM
Username: svc_insightvm_scan
Password: <service-account-password>
Authentication: NTLM
Privilege Elevation:
Type: None (use domain admin or local admin)
Linux/Unix Credentials (SSH)
Credential Type: Secure Shell (SSH)
Username: insightvm_scan
Authentication: SSH Key (preferred) or Password
SSH Private Key: /opt/rapid7/.ssh/scan_key
Port: 22
Privilege Elevation:
Type: sudo
sudo User: root
sudo Password: <sudo-password>
Database Credentials
Credential Type: Microsoft SQL Server
Instance: MSSQLSERVER
Domain: CORP
Username: insightvm_db_scan
Authentication: Windows Authentication
Credential Type: Oracle
Port: 1521
SID: ORCL
Username: insightvm_scan
Step 5: Configure Scan Templates
Custom scan template for balanced scanning:
Template Name: Enterprise-Standard-Scan
Service Discovery:
TCP Ports: Well-known (1-1024) + common services
UDP Ports: DNS(53), SNMP(161), NTP(123), TFTP(69)
Method: SYN scan (stealth)
Vulnerability Checks:
Safe checks only: Enabled
Skip potential: Disabled
Web spidering: Disabled (separate template)
Policy checks: Enabled (CIS benchmarks)
Performance:
Max parallel assets: 10
Max requests per second: 100
Timeout per asset: 30 minutes
Retries: 2
Step 6: Set Up Insight Agent Deployment
# Windows Agent Installation (via GPO or SCCM)
msiexec /i agentInstaller-x86_64.msi /quiet /norestart `
CUSTOMTOKEN=<platform-token> `
CUSTOMCONFIG=<agent-config>
# Linux Agent Installation
chmod +x agent_installer.sh
./agent_installer.sh install_start \
--token <platform-token>
# Verify agent connectivity
# Check InsightVM console: Assets > Agent Management
Step 7: Configure Remediation Workflows
Remediation Project:
Name: Q1-2025-Critical-Remediation
Scope:
Severity: Critical + High
CVSS Score: >= 7.0
Assets: Production-Network site
Assignment:
Team: Infrastructure-Ops
Due Date: 2025-03-31
Tracking:
Auto-verify: Enabled (re-scan on next scheduled scan)
Notification: Email on overdue items
Escalation: Manager notification at 75% SLA
Step 8: API Integration for Automation
import requests
import json
class InsightVMClient:
"""Rapid7 InsightVM API v3 client for automation."""
def __init__(self, console_url, api_key):
self.base_url = f"{console_url}/api/3"
self.session = requests.Session()
self.session.headers.update({
"Content-Type": "application/json",
"Authorization": f"Bearer {api_key}"
})
self.session.verify = not os.environ.get("SKIP_TLS_VERIFY", "").lower() == "true" # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
def get_sites(self):
"""List all configured scan sites."""
response = self.session.get(f"{self.base_url}/sites")
response.raise_for_status()
return response.json().get("resources", [])
def start_scan(self, site_id, engine_id=None, template_id=None):
"""Trigger an ad-hoc scan for a site."""
payload = {}
if engine_id:
payload["engineId"] = engine_id
if template_id:
payload["templateId"] = template_id
response = self.session.post(
f"{self.base_url}/sites/{site_id}/scans",
json=payload
)
response.raise_for_status()
return response.json()
def get_asset_vulnerabilities(self, asset_id):
"""Retrieve vulnerabilities for a specific asset."""
response = self.session.get(
f"{self.base_url}/assets/{asset_id}/vulnerabilities"
)
response.raise_for_status()
return response.json().get("resources", [])
def get_scan_status(self, scan_id):
"""Check the status of a running scan."""
response = self.session.get(f"{self.base_url}/scans/{scan_id}")
response.raise_for_status()
return response.json()
def create_remediation_project(self, name, description, assets, vulns):
"""Create a remediation tracking project."""
payload = {
"name": name,
"description": description,
"assets": {"includedTargets": {"addresses": assets}},
"vulnerabilities": {"includedVulnerabilities": vulns}
}
response = self.session.post(
f"{self.base_url}/remediations",
json=payload
)
response.raise_for_status()
return response.json()
# Usage
client = InsightVMClient("https://insightvm-console:3780", "api-key-here")
sites = client.get_sites()
for site in sites:
print(f"Site: {site['name']} - Assets: {site.get('assets', 0)}")
Best Practices
- Deploy Scan Engines close to target networks to minimize scan traffic traversing firewalls
- Use Insight Agents for roaming laptops and remote workers that are not always reachable by network scans
- Combine agent-based and engine-based scanning for the most accurate vulnerability view
- Configure scan blackout windows during business-critical hours to avoid operational impact
- Use credential testing before full scans to validate authentication works
- Enable safe checks to prevent accidental denial of service on production systems
- Separate scan sites by network segment, business unit, or compliance scope
- Leverage tag-based asset groups for dynamic reporting and remediation tracking
Common Pitfalls
- Running full scans during business hours causing network congestion or service degradation
- Using unauthenticated scans only, missing 60-80% of local vulnerabilities
- Not excluding fragile devices (printers, ICS/SCADA, medical devices) from aggressive scan templates
- Failing to distribute Scan Engines across network segments, causing firewall bottlenecks
- Ignoring scan engine resource utilization leading to incomplete scans
- Not configuring scan duration limits, allowing runaway scans to consume resources indefinitely
Related Skills
- performing-agentless-vulnerability-scanning
- building-vulnerability-data-pipeline-with-api
- implementing-wazuh-for-vulnerability-detection
- performing-remediation-validation-scanning
How to use implementing-rapid7-insightvm-for-scanning on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add implementing-rapid7-insightvm-for-scanning
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches implementing-rapid7-insightvm-for-scanning from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate implementing-rapid7-insightvm-for-scanning. Access the skill through slash commands (e.g., /implementing-rapid7-insightvm-for-scanning) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★64 reviews- ★★★★★Arya Jain· Dec 28, 2024
Keeps context tight: implementing-rapid7-insightvm-for-scanning is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Liam Singh· Dec 24, 2024
Useful defaults in implementing-rapid7-insightvm-for-scanning — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yusuf Malhotra· Dec 24, 2024
I recommend implementing-rapid7-insightvm-for-scanning for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ren Malhotra· Nov 19, 2024
implementing-rapid7-insightvm-for-scanning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Yusuf Reddy· Nov 15, 2024
I recommend implementing-rapid7-insightvm-for-scanning for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ren Ndlovu· Nov 15, 2024
Solid pick for teams standardizing on skills: implementing-rapid7-insightvm-for-scanning is focused, and the summary matches what you get after install.
- ★★★★★Liam Thomas· Nov 15, 2024
Useful defaults in implementing-rapid7-insightvm-for-scanning — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Arya Khan· Oct 10, 2024
implementing-rapid7-insightvm-for-scanning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Liam Khan· Oct 6, 2024
implementing-rapid7-insightvm-for-scanning reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kwame Torres· Oct 6, 2024
We added implementing-rapid7-insightvm-for-scanning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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