AynOps

📚 Table of Contents

• Overview
• Features
• Tools & Prompts
• Demo
• Prerequisites
• Installation
• Usage
• Examples
• Registries & Ecosystem
• LLM Client Behavior & Security Considerations
• Legal & Ethical Usage
• Known Limitations
• Project Structure
• Roadmap & Discussions
• Contributing
• License
• Author

🌐 Overview

Modern Large Language Models (LLMs) operate in sandboxed, non-deterministic execution environments devoid of native raw-socket access, packet crafting abilities, and live network reconnaissance primitives. When tasked with auditing an attack surface, AI agents are fundamentally constrained by static training cutoff boundaries and an inability to query live network interfaces.

AynOps bridges this operational divide. Built on top of the Model Context Protocol (MCP) specification using the FastMCP framework, AynOps exposes an extensible suite of deterministic cybersecurity reconnaissance tools directly to LLM agents (such as Cursor, VS Code, Claude Desktop, and custom MCP clients) over a standard JSON-RPC 2.0 stdio transport.

┌───────────────────────────┐         JSON-RPC 2.0 (stdio)         ┌────────────────────────────────────────────────────────┐
│     AI / LLM Client       │ ◄──────────────────────────────────► │                   AynOps MCP Server                    │
│ (Cursor, VS Code, Claude) │                                      │                   (Local Daemon)                       │
└───────────────────────────┘                                      └──────────────────────────┬─────────────────────────────┘
                                                                                              │
                                  ┌───────────────────────────────────────────────────────────┴──────────────────────────────┐
                                  ▼                                                           ▼                              ▼
                    ┌──────────────────────────┐                                ┌──────────────────────────┐   ┌──────────────────────────┐
                    │      Active Network      │                                │       DNS & OSINT        │   │    Web & Cloud Audits    │
                    │  • Nmap Port Scanner     │                                │   • Authoritative WHOIS  │   │   • SSL/TLS X.509 Parser │
                    │  • Team Cymru ASN BGP    │                                │   • dnspython Resolver   │   │   • HTTP Headers Linting │
                    │  • AbuseIPDB Threat Intel│                                │   • crt.sh CT Log Parser │   │   • Multi-Cloud Storage  │
                    └──────────────────────────┘                                │   • Subdomain Takeover   │   │   • Redirect Hop Tracer  │
                                                                                └──────────────────────────┘   └──────────────────────────┘

Architectural Highlights

• 🔒 Local-First & Zero-Telemetry Egress: AynOps runs entirely as a local daemon on your host or isolated container. Target queries, raw IP telemetry, and internal host details never transit through external telemetry collectors.
• ⚡ Wave-Based Concurrent Pipeline: Orchestrates complex reconnaissance tasks through an intelligent 3-wave async execution model, resolving foundational network primitives in parallel before dispatching dependent service, web, and threat intelligence probes.
• 🧠 Unified Heuristic Signal Extraction: Raw outputs from heterogeneous utilities (Nmap, dnspython, cryptography, socket APIs) are parsed and normalized into structured threat signals (tools/signals/) designed for direct ingestion by LLM reasoning layers.
• 🛠️ Standardized MCP Tool & Prompt Primitives: Fully compliant with the official Model Context Protocol standard, packaging ready-to-use tool declarations and meta-prompts (threat_analysis) for automated threat modeling workflows.

⚡ Features

AynOps is engineered from the ground up for cybersecurity engineers, red/blue teams, and security researchers seeking deterministic automation:

• 🔒 100% Local-First Execution: Self-hosted local daemon with zero telemetry leaks, preserving target confidentiality.
• ⚡ 3-Wave Asynchronous Recon Engine: Parallelized execution pipeline reducing end-to-end full recon duration by up to 70%.
• 🧠 Structured Heuristic Signal Extraction: Translates raw terminal outputs into normalized, machine-readable threat telemetry.
• 🌐 Comprehensive Perimeter Mapping: Authoritative WHOIS (RFC 3912), comprehensive DNS RR resolution, and multi-threaded dictionary subdomain brute-forcing.
• 📜 Certificate Transparency Log Extraction: Real-time querying of crt.sh append-only Merkle trees for passive subdomain and wildcard SSL discovery.
• 🛡️ Dangling DNS & Subdomain Takeover Detection: Resolves CNAME pointers against signatures for 8+ cloud providers (AWS S3, Azure, GitHub Pages, Heroku, Shopify, Fastly, Ghost, Pantheon) with active HTTP verification probing.
• ☁️ Multi-Cloud Storage Bucket Auditing: Generates domain keyword permutations and checks for unauthenticated public access across AWS S3, Azure Blob Storage, and Google Cloud Storage.
• 📧 RFC-Compliant Email Security Assessment: Audits SPF (v=spf1), DKIM selectors, and DMARC enforcement policies (p=reject/quarantine/none) with quantitative scoring (0–100).
• 🔍 Multi-Layer Web Technology Fingerprinting: 4-layer non-intrusive technology identification engine — HTTP response headers (Server, X-Powered-By, CDN markers), Set-Cookie cookie names, <meta name="generator"> tag parsing, and HTML/asset-path substring matching — returning structured detections with confidence scores (0–100) and evidence strings.
• 🔀 Deep HTTP Redirect Chain & TLS Downgrade Tracer: Hop-by-hop 3xx status analysis detecting SSL stripping, RFC 1918 internal IP address leaks, redirect loops, and cross-domain hops.
• 🎯 Real-Time NIST NVD v2 CVE Querying: Direct REST API integration with NIST National Vulnerability Database for CVSS v3.1/v2 base scores, CWE vectors, and exploit references without requiring API keys.
• 🤖 Cognitive Threat Analysis Meta-Prompt: Native MCP prompt template converting raw scan telemetry into prioritized vulnerability matrices and remediation roadmaps.
• 📦 Container-Ready Architecture: Bundles a production-ready Dockerfile with pre-installed Nmap and Python 3.12 for zero-dependency stdio deployment.

🛠️ Tools & Prompts

AynOps provides 17 specialized reconnaissance tools alongside structured MCP prompt templates.

1. Multi-Wave Automated Reconnaissance Pipeline (full_recon)

The full_recon tool orchestrates a 3-wave concurrent pipeline designed to minimize scan latency while preserving dependency order across tools:

Target Domain
     │
     ├──► [Wave 1: Base Perimeter] ──► whois_lookup + dns_enumeration + ssl_inspect + email_security_check + asn_lookup
     │
     ├──► [Wave 2: Service & Web]  ──► port_scan (service) + tech_stack_detect + headers_analyzer + cert_transparency
     │
     └──► [Wave 3: Threat Feeds]   ──► ip_reputation (AbuseIPDB - dynamically bound to resolved IP address)
     │
     ▼
Signal Aggregator (tools/signals/) ──► Unified Heuristic Security Telemetry Payload

2. Core Reconnaissance Tool Inventory (Included in full_recon)

Documentation status: Detailed reference pages are currently available for tools marked Available. Tools marked Pending are implemented and included in the inventory, but their dedicated documentation pages are not yet available.

3. Standalone Offensive Tools

4. Specialized AI Prompt Templates

📸 Demo

Single Tool Execution — CVE Vulnerability Lookup

Query known Common Vulnerabilities and Exposures (CVEs) directly against the NIST National Vulnerability Database (NVD) v2 API for specific software components and patch revisions without requiring API authentication:

Full Reconnaissance Pipeline — Parallelized Security Assessment

Trigger the 3-wave parallel execution engine to synthesize network perimeter state, DNS resource records, SSL/TLS posture, email anti-spoofing enforcement, HTTP headers, and software stack fingerprints into a single structured report:

📋 Prerequisites

Ensure your host environment meets the following runtime requirements before starting the server:

• Python Runtime: Python >= 3.12 (Download Python)
• Network Scanner: Nmap binary installed and accessible in the system PATH (Download Nmap)
• Environment & Package Manager: uv (Recommended for high-performance virtual environments, Install uv) or pip
• Version Control: Git (Download Git)
• MCP Client: Cursor, VS Code (MCP extension), Claude Desktop, Continue.dev, Zed, or any client implementing the MCP JSON-RPC protocol.
• Container Runtime (Optional): Docker Engine / Docker Desktop (Download Docker)

⚙️ Installation

Method 1: Local Virtual Environment Setup (Recommended)

Step 1: Clone the Repository

git clone https://github.com/AynOps/AynOps.git
cd AynOps

Step 2: Provision Virtual Environment and Install Dependencies

Using uv (Fastest):

uv sync

Alternatively, using standard Python tooling:

python -m venv .venv

# On Linux/macOS:
source .venv/bin/activate

# On Windows (PowerShell):
.venv\Scripts\Activate.ps1

pip install -e .

Step 3: Install and Configure Nmap

Nmap must be installed locally and registered in your system PATH for the port_scan tool:

1. Download and run the setup installer from nmap.org/download.html.
2. Add Nmap to your System PATH:
   • Open Start → Search Environment Variables → Click Edit the system environment variables.
   • Under System Variables, select Path → Click Edit → Click New.
   • Add the installation path: C:\Program Files (x86)\Nmap (or C:\Program Files\Nmap).
   • Confirm all dialogs by clicking OK.
3. Open a new PowerShell terminal and verify:

   nmap --version
   

Install via Homebrew:

brew install nmap
nmap --version

Install via your system package manager:

# Debian / Ubuntu / Kali
sudo apt update && sudo apt install -y nmap

# Fedora / RHEL
sudo dnf install -y nmap

# Arch Linux
sudo pacman -S nmap

Step 4: Configure Your MCP Client

Locate and edit your client's MCP configuration file (e.g., Claude Desktop, Cursor, or VS Code):

Insert the AynOps server stanza:

Windows (claude_desktop_config.json):

{
  "mcpServers": {
    "AynOps": {
      "command": "C:\\full\\path\\to\\AynOps\\.venv\\Scripts\\python.exe",
      "args": ["C:\\full\\path\\to\\AynOps\\server.py"],
      "env": {
        "ABUSEIPDB_API_KEY": "your-abuseipdb-api-key-here",
        "HIBP_API_KEY": "your-hibp-api-key-here"
      }
    }
  }
}

macOS / Linux (claude_desktop_config.json):

{
  "mcpServers": {
    "AynOps": {
      "command": "/full/path/to/AynOps/.venv/bin/python3",
      "args": ["/full/path/to/AynOps/server.py"],
      "env": {
        "ABUSEIPDB_API_KEY": "your-abuseipdb-api-key-here",
        "HIBP_API_KEY": "your-hibp-api-key-here"
      }
    }
  }
}

[!IMPORTANT] Always supply the absolute filesystem path to your .venv Python binary and server.py file to prevent the client from binding to a mismatched global Python runtime.

[!NOTE] ABUSEIPDB_API_KEY is optional and only required for ip_reputation (Obtain free at abuseipdb.com). HIBP_API_KEY is optional and only required for hibp_check (Obtain at haveibeenpwned.com/API/Key). All other tools operate with zero API keys.

Step 5: Launch Client and Validate Registration

1. Terminate all running client processes completely (ensure no background instances remain in Task Manager / Activity Monitor).
2. Re-open your MCP client.
3. Verify tool availability by querying:

   What cybersecurity tools do you have available?
   

Method 2: Containerized Deployment via Docker

AynOps ships a production-ready Dockerfile bundling Python 3.12, uv, and nmap, eliminating host dependency requirements.

Step 1: Build the Image

git clone https://github.com/AynOps/AynOps.git
cd AynOps
docker build -t aynops:latest .

Step 2: Verify Stdio Transport

Run a one-shot container execution. The container will initialize and listen for MCP JSON-RPC messages on stdin:

docker run --rm -i aynops:latest
# Press Ctrl+C to exit

Step 3: Configure Client for Docker Execution

Configure your client to spawn the container directly:

{
  "mcpServers": {
    "AynOps": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e", "ABUSEIPDB_API_KEY=your-abuseipdb-api-key-here",
        "-e", "HIBP_API_KEY=your-hibp-api-key-here",
        "aynops:latest"
      ]
    }
  }
}

[!TIP] The container must run in interactive mode (-i) without detached flags (-d) to preserve the standard I/O communication pipe required by the MCP protocol.

🚀 Usage

Natural Language Tool Invocations

Once connected, you can interact with individual reconnaissance tools or prompt compound analytical queries:

# Domain & DNS Reconnaissance
"Perform a WHOIS query on target-organization.com"
"Enumerate all DNS resource records and brute-force common subdomains for example.com"
"Query Certificate Transparency logs for *.internal.corp.com"

# Network & Host Fingerprinting
"Run a service version port scan on scanme.nmap.org"
"Execute an ASN lookup for IP 1.1.1.1 and identify the BGP prefix"
"Analyze the IP reputation of 198.51.100.42"

# Web Application & Perimeter Security
"Inspect the SSL/TLS certificate of payment.gateway.io on port 443"
"Audit the HTTP security headers of enterprise-app.com and report misconfigurations"
"Fingerprint the CMS, JavaScript frameworks, and CDNs running on target.com"
"Trace the HTTP redirect chain for http://insecure-portal.com"
"Inspect robots.txt on admin.target.com for unindexed endpoints"

# Vulnerability & Threat Audits
"Look up known CVEs for OpenSSH version 8.2p1"
"Check if cloud storage buckets exist for domain fincorp-assets.com"
"Detect dangling CNAME records susceptible to subdomain takeover on cloudapp.net"
"Query Have I Been Pwned for breaches associated with [email protected]"

Port Scan Profiles

The port_scan tool supports multiple scanning strategies tailored to operational velocity and assessment depth:

Example invocation:

Scan target scanme.nmap.org using the 'service' profile.

End-to-End Threat Modeling with threat_analysis Prompt

For optimal threat intelligence synthesis in supported MCP clients:

1. In your MCP client (e.g. Claude Desktop), click the + icon in the prompt input area.
2. Select Connectors → Add from AynOps.
3. Choose the threat_analysis prompt template.
4. Execute your target reconnaissance prompt:

   Do a complete security recon on scanme.nmap.org
   

5. The LLM will invoke full_recon, process the normalized signals, and generate a correlated matrix mapping open ports to potential CVEs, missing headers, email spoofability, and infrastructure risk factors.

💡 Examples

Scenario 1: CVE Patch Validation and Vulnerability Correlation

User: Run a service scan on our perimeter host and cross-reference detected software with NVD for CVEs.

Agent: [Invokes port_scan with scan_type="service"]
       Discovered Open Services:
       • Port 80/tcp: Apache httpd 2.4.49
       • Port 22/tcp: OpenSSH 7.4

       [Invokes cve_lookup for software="apache", version="2.4.49"]
       Critical Findings:
       • CVE-2021-41773 (CVSS 9.8) — Remote Code Execution via Path Traversal.
       
       Remediation: Upgrade Apache to >= 2.4.51 immediately.

Scenario 2: Dangling DNS Subdomain Takeover Triaging

User: Check our domain company.com for dangling DNS records pointing to abandoned cloud services.

Agent: [Invokes subdomain_takeover for "company.com"]
       Resolved CNAME: blog.company.com -> company-blog.ghost.io
       HTTP Verification Probe: HTTP 404 - "Fastly error: unknown domain company-blog.ghost.io"
       Status: HIGH RISK — Dangling CNAME pointer detected for Ghost/Fastly infrastructure.
       Remediation: Remove the orphaned CNAME DNS record from your authoritative nameservers.

Scenario 3: Unauthenticated Multi-Cloud Storage Exposure

User: Does our company domain 'megacorp.io' expose any unauthenticated cloud storage containers?

Agent: [Invokes cloud_exposure_check for "megacorp.io"]
       Probing Permutations across AWS S3, Azure Blob, GCS...
       • https://megacorp-data.s3.amazonaws.com — AccessDenied (Secured)
       • https://megacorp-backup.s3.amazonaws.com — HTTP 200 OK (PUBLIC LISTING ENABLED)
         Found XML object keys: /db_dump_2025.sql.gz, /employee_records.csv
       • https://megacorp.blob.core.windows.net/public — HTTP 404 (Not Found)

Scenario 4: HTTP Redirect Chain & TLS Downgrade Analysis

User: Trace the redirect chain for http://legacy.portal.org

Agent: [Invokes trace_redirects for "http://legacy.portal.org"]
       Hop 1: http://legacy.portal.org (301 Moved Permanently)
       Hop 2: https://auth.portal.org/login (302 Found)
       Hop 3: http://internal-sso.portal.org/auth (302 Found) ⚠️ TLS DOWNGRADE DETECTED
       Hop 4: http://192.168.1.50/dashboard ⚠️ PRIVATE IP LEAK (RFC 1918)

📦 Registries & Ecosystem

AynOps is indexed and distributed across the following official registries:

🤖 LLM Client Behavior & Security Considerations

AynOps acts strictly as an execution server over MCP. Tool invocation decisions, parameter synthesis, and prompt chaining are governed by the connected LLM client:

• Model Guardrails & Safety Filters: Certain LLM providers (e.g., Anthropic, OpenAI) enforce heuristic safety filters. Queries like "Attack example.com" or even standard phrases like "Port scan example.com" can occasionally be declined by commercial frontends.
  • Workaround 1: Phrase prompts defensively (e.g., "Audit the defensive security posture of our infrastructure at example.com").
  • Workaround 2: Connect AynOps to developer-centric or unconstrained MCP clients (e.g., Cursor, Continue.dev with local models via Ollama, or our upcoming AynOps Dedicated Client).
• Execution Timeouts: Large operations (such as full port scans across all 65,535 ports) may exceed default client JSON-RPC timeout thresholds (typically 60–120s). Configure tool timeouts appropriately or use the basic and service profiles.
• Parameter Extraction Fidelity: Ensure your prompts provide fully qualified domain names (FQDNs) or valid IPv4/IPv6 addresses to prevent client-side schema validation errors.

⚖️ Legal & Ethical Usage

[!CAUTION] Only perform active network scans and vulnerability evaluations against infrastructure you own or have explicit, documented authorization to test.

• Passive OSINT Tools (whois_lookup, dns_enumeration, asn_lookup, cert_transparency, headers_analyzer, cve_lookup, robots_txt_inspect): Operate purely against publicly advertised records and standard web responses.
• Active Scanning Tools (port_scan, cloud_exposure_check, subdomain_takeover): Send active network traffic. Unauthorized scanning may violate the Computer Fraud and Abuse Act (CFAA), GDPR, and local cyber regulations.
• Authorized Testing Host: For testing and demonstration purposes without production risk, use the officially designated Nmap test host: scanme.nmap.org.

⚠️ Known Limitations

• OS Detection Privileges: Nmap OS fingerprinting (scan_type="os") relies on raw TCP/IP socket crafting (-O) and requires elevated root / Administrator privileges on the host system.
• Upstream OSINT Rate Limiting: Tools interfacing with third-party endpoints (e.g., crt.sh Certificate Transparency logs, NIST NVD v2 REST API) are subject to upstream service availability and rate limits.
• API Key Dependencies: ip_reputation and hibp_check require valid API keys configured in environment variables to return live telemetry.
• Container Stdio Lifecycle: When deploying via Docker, the container must maintain an attached interactive stdio stream; running in detached daemon mode (-d) will terminate the MCP pipe.

🗂️ Project Structure

AynOps/
├── .github/                  # GitHub workflows, templates, and assets
│   ├── images/               # Documentation and demo media
│   ├── ISSUE_TEMPLATE/       # Issue templates
│   └── workflows/            # CI/CD workflows
├── docs/                     # Extended documentation
│   └── tools/                # Per-tool documentation
├── tests/                    # Unit and integration tests
├── tools/                    # MCP reconnaissance tools
│   ├── fingerprint/          # Web technology fingerprinting engine
│   ├── prompts/              # MCP prompt templates
│   └── signals/              # Signal extraction framework
├── utils/                    # Shared utilities and helpers
├── .env.example              # Environment variable template
├── .gitignore                # Git ignored files and directories
├── .python-version           # Mentions the Python version
├── CONTRIBUTING.md           # Contribution guidelines
├── Dockerfile                # Container configuration
├── glama.json                # Glama MCP configuration
├── LICENSE                   # MIT License
├── mcp.json                  # MCP metadata and tool manifests
├── pyproject.toml            # Project metadata and dependencies
├── README.md                 # Project documentation
├── SECURITY.md               # Security policy
├── server.json               # MCP server package schema
├── server.py                 # FastMCP server entrypoint
└── uv.lock                   # Dependency lockfile

🗺️ Roadmap & Discussions

We have active GitHub Discussions set up for community ideas, RFCs, tool requests, and roadmap planning.

Check the current planned Roadmap

💬 Join the conversation: Share your feedback, suggest new tools, or vote on roadmap features in AynOps GitHub Discussions!

🤝 Contributing

Contributions, issues, and feature requests are welcome! See CONTRIBUTING.md for the development setup, project structure, and a guide to adding support for a new tool — that's currently the highest-value place to contribute. All participants are expected to follow Code of Conduct.

Quick version:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes
4. Push to the branch and open a Pull Request

If you find a bug or a security vulnerability, please report it directly to the developer.

📜 License

This project is licensed under the MIT License — see the LICENSE file for details. You are free to use, modify, distribute, and integrate this software in commercial and open-source environments.

👤 Author

Gaohar Imran

• GitHub: @gaoharimran29-glitch
• LinkedIn: Gaohar Imran

Special thanks to @SemTiOne, one of the early contributors to AynOps, for his valuable contributions, reviews, feedback, and continued support throughout the project. His help, especially during the early stages, is genuinely appreciated. ❤️

Special thanks to @Nitjsefnie for their continued and active involvement in AynOps through multiple contributions across the project. Their consistent effort, initiative, and willingness to improve the project are greatly appreciated. ❤️

With gratitude to all our contributors who have helped improve AynOps through code, documentation, testing, ideas, reviews, and feedback. Your contributions are genuinely appreciated. ❤️