performing-agentless-vulnerability-scanning▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Configure and execute agentless vulnerability scanning using network protocols, cloud snapshot analysis, and API-based discovery to assess systems without installing endpoint agents.
| name | performing-agentless-vulnerability-scanning |
| description | Configure and execute agentless vulnerability scanning using network protocols, cloud snapshot analysis, and API-based discovery to assess systems without installing endpoint agents. |
| domain | cybersecurity |
| subdomain | vulnerability-management |
| tags | - agentless-scanning - vulnerability-assessment - cloud-security - ssh - wmi - snapshot-analysis - vuls - tenable |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_ai_rmf | - GOVERN-1.1 - MEASURE-2.7 - MANAGE-3.1 |
| nist_csf | - ID.RA-01 - ID.RA-02 - ID.IM-02 - ID.RA-06 |
Performing Agentless Vulnerability Scanning
Overview
Agentless vulnerability scanning assesses systems for security weaknesses without requiring endpoint agent installation. This approach leverages existing network protocols (SSH for Linux, WMI for Windows), cloud provider APIs for snapshot-based analysis, and authenticated remote checks. Modern cloud platforms like Microsoft Defender for Cloud, Wiz, Datadog, and Tenable perform out-of-band analysis by taking disk snapshots and examining OS configurations and installed packages offline. The open-source tool Vuls provides agentless scanning based on NVD and OVAL data for Linux/FreeBSD systems. This skill covers configuring agentless scans across on-premises, cloud, and containerized environments.
When to Use
- When conducting security assessments that involve performing agentless vulnerability scanning
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing
Prerequisites
- SSH key-based authentication configured on Linux/Unix targets
- WMI/WinRM access on Windows targets with appropriate credentials
- Cloud provider API credentials (AWS IAM, Azure RBAC, GCP IAM)
- Network access from scanner to target systems on required ports
- Service account with read-only access to target system configurations
- Python 3.8+ for custom scanning automation
Core Concepts
Agentless vs Agent-Based Scanning
| Aspect | Agentless | Agent-Based |
|---|---|---|
| Deployment | No software installation needed | Agent install on every endpoint |
| Network dependency | Requires network connectivity | Works offline with cloud sync |
| Performance impact | Minimal on target systems | Light continuous overhead |
| Coverage depth | Depends on protocol/credentials | Deep local access |
| Cloud snapshot analysis | Native capability | Not applicable |
| Ideal for | Cloud VMs, IoT, legacy systems, OT | Managed endpoints, laptops |
Agentless Scanning Methods
| Method | Protocol | Target OS | Port | Use Case |
|---|---|---|---|---|
| SSH Remote Commands | SSH | Linux/Unix | 22 | Package enumeration, config audit |
| WMI Remote Query | WMI/DCOM | Windows | 135, 445 | Hotfix enumeration, registry checks |
| WinRM PowerShell | WS-Man | Windows | 5985/5986 | Remote command execution |
| SNMP Community | SNMP v2c/v3 | Network devices | 161 | Device fingerprinting, firmware check |
| Cloud Snapshot | Provider API | Cloud VMs | N/A | Disk image analysis |
| Container Registry | HTTPS | Container images | 443 | Image vulnerability scanning |
| API-Based | REST/HTTPS | SaaS/Cloud | 443 | Configuration assessment |
Cloud Snapshot Analysis Flow
1. Scanner requests disk snapshot via cloud API
2. Cloud provider creates snapshot of VM root + data disks
3. Scanner mounts snapshot in isolated analysis environment
4. Scanner examines OS packages, configurations, file system
5. Snapshot is deleted after analysis (no persistent copies)
6. Results sent to central management console
Workflow
Step 1: SSH-Based Agentless Scanning (Linux)
# Create dedicated scan SSH key pair
ssh-keygen -t ed25519 -f /opt/scanner/.ssh/scan_key -N "" \
-C "[email protected]"
# Deploy public key to targets via Ansible
# ansible-playbook deploy_scan_key.yml
# Test connectivity to target
ssh -i /opt/scanner/.ssh/scan_key -o ConnectTimeout=10 \
scanner@target-host "cat /etc/os-release && dpkg -l 2>/dev/null || rpm -qa"
import paramiko
import json
class AgentlessLinuxScanner:
"""SSH-based agentless vulnerability scanner for Linux systems."""
def __init__(self, key_path):
self.key_path = key_path
def connect(self, hostname, username="scanner", port=22):
"""Establish SSH connection to target."""
client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
key = paramiko.Ed25519Key.from_private_key_file(self.key_path)
client.connect(hostname, port=port, username=username, pkey=key,
timeout=30, banner_timeout=30)
return client
def get_os_info(self, client):
"""Detect OS type and version."""
_, stdout, _ = client.exec_command("cat /etc/os-release", timeout=10)
os_release = stdout.read().decode()
info = {}
for line in os_release.strip().split("\n"):
if "=" in line:
key, val = line.split("=", 1)
info[key] = val.strip('"')
return info
def get_installed_packages(self, client):
"""Enumerate installed packages."""
# Try dpkg (Debian/Ubuntu)
_, stdout, _ = client.exec_command(
"dpkg-query -W -f='${Package}|${Version}|${Architecture}\\n'",
timeout=30
)
output = stdout.read().decode().strip()
if output:
packages = []
for line in output.split("\n"):
parts = line.split("|")
if len(parts) >= 2:
packages.append({
"name": parts[0],
"version": parts[1],
"arch": parts[2] if len(parts) > 2 else "",
"manager": "dpkg"
})
return packages
# Try rpm (RHEL/CentOS/Fedora)
_, stdout, _ = client.exec_command(
"rpm -qa --queryformat '%{NAME}|%{VERSION}-%{RELEASE}|%{ARCH}\\n'",
timeout=30
)
output = stdout.read().decode().strip()
packages = []
for line in output.split("\n"):
parts = line.split("|")
if len(parts) >= 2:
packages.append({
"name": parts[0],
"version": parts[1],
"arch": parts[2] if len(parts) > 2 else "",
"manager": "rpm"
})
return packages
def check_kernel_version(self, client):
"""Get running kernel version."""
_, stdout, _ = client.exec_command("uname -r", timeout=10)
return stdout.read().decode().strip()
def check_listening_ports(self, client):
"""Enumerate listening network services."""
_, stdout, _ = client.exec_command(
"ss -tlnp 2>/dev/null || netstat -tlnp 2>/dev/null",
timeout=10
)
return stdout.read().decode().strip()
def scan_host(self, hostname, username="scanner"):
"""Perform full agentless scan of a host."""
print(f"[*] Scanning {hostname}...")
client = self.connect(hostname, username)
result = {
"hostname": hostname,
"os_info": self.get_os_info(client),
"kernel": self.check_kernel_version(client),
"packages": self.get_installed_packages(client),
"listening_ports": self.check_listening_ports(client),
}
client.close()
print(f" [+] Found {len(result['packages'])} packages on {hostname}")
return result
Step 2: WinRM-Based Agentless Scanning (Windows)
import winrm
class AgentlessWindowsScanner:
"""WinRM-based agentless vulnerability scanner for Windows."""
def __init__(self, username, password, domain=None):
self.username = username
self.password = password
self.domain = domain
def connect(self, hostname, use_ssl=True):
"""Create WinRM session."""
port = 5986 if use_ssl else 5985
transport = "ntlm"
user = f"{self.domain}\\{self.username}" if self.domain else self.username
session = winrm.Session(
f"{'https' if use_ssl else 'http'}://{hostname}:{port}/wsman",
auth=(user, self.password),
transport=transport,
server_cert_validation="ignore"
)
return session
def get_installed_hotfixes(self, session):
"""Get installed Windows updates/hotfixes."""
cmd = "Get-HotFix | Select-Object HotFixID,InstalledOn,Description | ConvertTo-Json"
result = session.run_ps(cmd)
if result.status_code == 0:
return json.loads(result.std_out.decode())
return []
def get_installed_software(self, session):
"""Enumerate installed software from registry."""
cmd = """
$paths = @(
'HKLM:\\SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\*',
'HKLM:\\SOFTWARE\\WOW6432Node\\Microsoft\\Windows\\CurrentVersion\\Uninstall\\*'
)
Get-ItemProperty $paths -ErrorAction SilentlyContinue |
Where-Object {$_.DisplayName} |
Select-Object DisplayName, DisplayVersion, Publisher |
ConvertTo-Json
"""
result = session.run_ps(cmd)
if result.status_code == 0:
return json.loads(result.std_out.decode())
return []
def get_os_info(self, session):
"""Get Windows OS details."""
cmd = "Get-CimInstance Win32_OperatingSystem | Select-Object Caption,Version,BuildNumber,OSArchitecture | ConvertTo-Json"
result = session.run_ps(cmd)
if result.status_code == 0:
return json.loads(result.std_out.decode())
return {}
def scan_host(self, hostname):
"""Perform full agentless scan of Windows host."""
print(f"[*] Scanning {hostname} via WinRM...")
session = self.connect(hostname)
result = {
"hostname": hostname,
"os_info": self.get_os_info(session),
"hotfixes": self.get_installed_hotfixes(session),
"software": self.get_installed_software(session),
}
print(f" [+] Found {len(result['hotfixes'])} hotfixes, "
f"{len(result['software'])} software entries")
return result
Step 3: Cloud Snapshot Scanning (AWS)
import boto3
import time
class AWSSnapshotScanner:
"""AWS EC2 agentless snapshot-based vulnerability scanner."""
def __init__(self, region="us-east-1"):
self.ec2 = boto3.client("ec2", region_name=region)
def create_snapshot(self, volume_id, description="Security scan snapshot"):
"""Create EBS snapshot for analysis."""
snapshot = self.ec2.create_snapshot(
VolumeId=volume_id,
Description=description,
TagSpecifications=[{
"ResourceType": "snapshot",
"Tags": [
{"Key": "Purpose", "Value": "VulnScan"},
{"Key": "AutoDelete", "Value": "true"},
]
}]
)
snapshot_id = snapshot["SnapshotId"]
print(f" [*] Creating snapshot {snapshot_id} from {volume_id}...")
waiter = self.ec2.get_waiter("snapshot_completed")
waiter.wait(SnapshotIds=[snapshot_id])
print(f" [+] Snapshot {snapshot_id} ready")
return snapshot_id
def delete_snapshot(self, snapshot_id):
"""Clean up snapshot after analysis."""
self.ec2.delete_snapshot(SnapshotId=snapshot_id)
print(f" [+] Deleted snapshot {snapshot_id}")
def scan_instance(self, instance_id):
"""Scan an EC2 instance via snapshot analysis."""
print(f"[*] Agentless scan of instance {instance_id}")
instance = self.ec2.describe_instances(
InstanceIds=[instance_id]
)["Reservations"][0]["Instances"][0]
root_volume = None
for bdm in instance.get("BlockDeviceMappings", []):
if bdm["DeviceName"] == instance.get("RootDeviceName"):
root_volume = bdm["Ebs"]["VolumeId"]
break
if not root_volume:
print(" [!] No root volume found")
return None
snapshot_id = self.create_snapshot(root_volume)
try:
# Analysis would be performed here
# Mount snapshot, examine packages, check configs
result = {
"instance_id": instance_id,
"snapshot_id": snapshot_id,
"root_volume": root_volume,
"platform": instance.get("Platform", "linux"),
"state": instance["State"]["Name"],
}
return result
finally:
self.delete_snapshot(snapshot_id)
Step 4: Vuls Open-Source Agentless Scanner
# /etc/vuls/config.toml - Vuls configuration for agentless scanning
[servers]
[servers.web-server-01]
host = "192.168.1.10"
port = "22"
user = "vuls"
keyPath = "/opt/vuls/.ssh/scan_key"
scanMode = ["fast"]
[servers.db-server-01]
host = "192.168.1.20"
port = "22"
user = "vuls"
keyPath = "/opt/vuls/.ssh/scan_key"
scanMode = ["fast-root"]
[servers.db-server-01.optional]
[servers.db-server-01.optional.sudo]
password = ""
[servers.container-host-01]
host = "192.168.1.30"
port = "22"
user = "vuls"
keyPath = "/opt/vuls/.ssh/scan_key"
scanMode = ["fast"]
containersIncluded = ["${running}"]
# Run Vuls agentless scan
vuls scan
# Generate report
vuls report -format-json -to-localfile
# View results
vuls tui
Best Practices
- Use SSH key-based authentication instead of passwords for Linux scanning
- Create dedicated service accounts with minimal read-only privileges for scanning
- Always clean up cloud snapshots after analysis to avoid storage costs and data exposure
- Combine agentless scanning with agent-based for comprehensive coverage
- Schedule scans during low-activity periods to minimize any performance impact
- Rotate scanning credentials regularly and store in a secrets vault
- Test scanner connectivity before scheduling production scans
- Use SNMPv3 with authentication and encryption for network device scanning
Common Pitfalls
- Using shared credentials across multiple environments without proper segmentation
- Not cleaning up temporary snapshots in cloud environments
- Assuming agentless scanning has zero performance impact (network and CPU are used)
- Missing WinRM/SSH firewall rules causing scan failures on new deployments
- Not accounting for SSH host key changes causing authentication failures
- Scanning OT/ICS devices with protocols they cannot safely handle
Related Skills
- implementing-rapid7-insightvm-for-scanning
- implementing-wazuh-for-vulnerability-detection
- deploying-osquery-for-endpoint-monitoring
- performing-remediation-validation-scanning
How to use performing-agentless-vulnerability-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 performing-agentless-vulnerability-scanning
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-agentless-vulnerability-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 performing-agentless-vulnerability-scanning. Access the skill through slash commands (e.g., /performing-agentless-vulnerability-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
4.7★★★★★37 reviews- ★★★★★Pratham Ware· Dec 28, 2024
I recommend performing-agentless-vulnerability-scanning for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Liam Chen· Dec 28, 2024
Registry listing for performing-agentless-vulnerability-scanning matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Ira Bansal· Dec 20, 2024
Solid pick for teams standardizing on skills: performing-agentless-vulnerability-scanning is focused, and the summary matches what you get after install.
- ★★★★★Ishan Dixit· Dec 16, 2024
Keeps context tight: performing-agentless-vulnerability-scanning is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chaitanya Patil· Dec 4, 2024
We added performing-agentless-vulnerability-scanning from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Piyush G· Nov 23, 2024
performing-agentless-vulnerability-scanning fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Kofi Chen· Nov 23, 2024
I recommend performing-agentless-vulnerability-scanning for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Michael Tandon· Nov 19, 2024
Solid pick for teams standardizing on skills: performing-agentless-vulnerability-scanning is focused, and the summary matches what you get after install.
- ★★★★★Li Dixit· Nov 7, 2024
performing-agentless-vulnerability-scanning is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Li Sethi· Oct 26, 2024
Useful defaults in performing-agentless-vulnerability-scanning — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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