CrowdSentinel MCP Server

<!-- mcp-name: io.github.thomasxm/crowdsentinel-mcp-server -->

<div align="center"> <img src="https://raw.githubusercontent.com/thomasxm/crowdsentinel-assets/main/logo.png" alt="CrowdSentinel Logo" width="400"/>

CrowdSentinel MCP Server

AI-Powered Threat Hunting & Incident Response Framework

      


MCP Official Registry · PyPI Package

Open-source threat hunting orchestrator connecting LLMs to enterprise security data via Model Context Protocol (MCP)

Quick Start · Installation · CLI Usage · Features · Architecture · Documentation · Examples

</div>

Warning This project is in active development and intended for security testing, research, and educational purposes only. It is not production-ready. Do not deploy in production environments. APIs, tool interfaces, and data formats may change without notice. Use at your own risk.

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Demo

https://github.com/user-attachments/assets/0d0381f0-5b68-43b2-8630-19ec130885b2

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What is CrowdSentinel?

CrowdSentinel transforms traditional SIEM querying into intelligent, framework-driven investigations using natural language. It serves as a unified security intelligence layer that connects large language models to enterprise security data sources, enabling:

• Natural Language Threat Hunting — Query Elasticsearch using plain English
• AI-Guided Investigation Workflows — Built-in prompts guide agents through proper IR methodology
• Persistent Investigation State — Memory-managed IoC tracking, forensic timelines, and cross-query correlation that survives across sessions (8GB FIFO storage)
• Cross-Tool IoC Correlation — IoCs discovered in one tool are automatically available to all others
• Multi-Source Analysis — Elasticsearch, EVTX logs (Chainsaw), PCAP files (Wireshark), live endpoint forensics (Velociraptor), local host forensics (osquery)
• Velociraptor Endpoint Forensics — 25 MCP tools for live artefact collection (processes, network, persistence, execution evidence, NTFS MFT, SRUM, remote EVTX) with automatic IoC extraction
• Encoded-Command Deobfuscation — Automatically peels base64/UTF-16LE/gzip/hex layers off encoded commands (e.g. PowerShell -EncodedCommand) so C2 addresses hidden inside them surface as IoCs
• Adaptive Schema Intelligence — Data-driven schema registry (ECS, Sysmon, Windows Security, auditd, Zeek, Packetbeat, AWS CloudTrail) adapts hunts to each index's field conventions
• DFIR Knowledge Resources — 9 MCP resources exposing investigation playbooks, Pyramid of Pain reference, and cross-correlation guidance directly to connected AI agents
• Standalone CLI — Full threat hunting from the terminal without an MCP client

---

<a name="installation"></a>

Installation

Install from PyPI (recommended)

# Install with pip
pip install crowdsentinel-mcp-server

# Or install with uv
uv pip install crowdsentinel-mcp-server

# Download detection rules, Chainsaw, and Sigma rules (one-time)
crowdsentinel setup

# Validate connector configuration and readiness (offline, no external calls)
crowdsentinel doctor

Optional extras:

# Velociraptor live endpoint forensics (adds 25 MCP tools)
pip install 'crowdsentinel-mcp-server[velociraptor]'

# Long-tail deobfuscation coverage via chepy (XOR, exotic encodings).
# The stdlib decoder core works without this — chepy only extends it.
pip install 'crowdsentinel-mcp-server[deobf]'

Detection rules (6,060 Lucene + EQL + ES|QL) are bundled with the package — no download needed. The setup command downloads additional tools:

• Chainsaw binary for EVTX analysis
• 3,000+ Sigma rules for Chainsaw

Downloaded tools are stored in ~/.crowdsentinel/ and persist across package upgrades.

System dependency for PCAP analysis:

# Required for network traffic analysis and cross-tool IoC correlation
sudo apt install tshark    # Debian/Ubuntu/Kali
sudo dnf install wireshark-cli  # Fedora/RHEL
brew install wireshark     # macOS

Run directly with uvx (no install needed)

# Elasticsearch 8.x (default)
uvx crowdsentinel-mcp-server

# Other backends
uvx crowdsentinel-mcp-server-es7   # Elasticsearch 7.x
uvx crowdsentinel-mcp-server-es9   # Elasticsearch 9.x
uvx opensearch-mcp-server          # OpenSearch 1.x/2.x/3.x

Install from source

git clone https://github.com/thomasxm/CrowdSentinels-AI-MCP.git
cd CrowdSentinels-AI-MCP
chmod +x setup.sh && ./setup.sh

# Or install everything, including all optional extras (deobf/chepy, velociraptor)
./setup.sh --full

The setup script will:

• Install dependencies (pipx, uv, Claude Code CLI if needed)
• Bundle 6,060 detection rules and download Chainsaw binary
• Prompt for Elasticsearch credentials (never hardcoded)
• Configure the MCP server with Claude Code
• Validate your connection

With --full, every optional extra is installed too, so all 139 MCP tools have their dependencies available — the recommended mode for a production-ready server.

Installed Size

CrowdSentinel bundles 6,060 detection rules and integrates with external analysis tools. Below is the full disk space breakdown so you can plan accordingly.

Core package (via pip or uvx):

| Component | Size | Notes | |---|---|---| | CrowdSentinel package | 49 MB | The server itself | | — Bundled Sigma rules (src/rules/) | 30 MB | 6,060 pre-converted detection rules | | — Elastic TOML rules (src/detection-rules/) | 17 MB | Original TOML format rules + hunting queries | | — Python code (clients, tools, etc.) | 2 MB | Actual application code | | Dependencies | 64 MB | All transitive deps | | — cryptography | 14 MB | Largest dependency (TLS) | | — elasticsearch | 8.3 MB | ES Python client | | — pygments | 5.2 MB | Syntax highlighting | | — pydantic_core | 5 MB | Validation engine | | — opensearchpy | 3.6 MB | OpenSearch client | | — Others (27 packages) | ~28 MB | mcp, fastmcp, httpx, anthropic, etc. | | Core total | 113 MB | pip install crowdsentinel-mcp-server |

Additional tools (via crowdsentinel setup):

| Component | Download | Installed | Notes | |---|---|---|---| | Chainsaw binary (v2.13.1) | ~3 MB | ~15 MB | EVTX log analysis engine | | Sigma rules (SigmaHQ) | ~3 MB | ~30 MB | 3,000+ Sigma rules for Chainsaw | | Chainsaw mappings | — | <1 MB | Event log source mappings | | Setup total | ~6 MB | ~46 MB | Stored in ~/.crowdsentinel/ |

System dependency (via package manager):

| Component | Installed | Install Command | Notes | |---|---|---|---| | tshark + Wireshark libs | ~132 MB | sudo apt install tshark | PCAP network analysis — required for cross-tool IoC correlation |

Full installation summary:

| Scenario | Total Disk Space | |---|---| | Core only (pip install) | ~113 MB | | Core + setup (crowdsentinel setup) | ~159 MB | | Full platform (+ tshark) | ~291 MB |

Note: PyPI download size is only 8.9 MB (wheel) thanks to compression of the bundled detection rules.

---

<a name="quick-start"></a>

Quick Start

1. Set environment variables

export ELASTICSEARCH_HOSTS="https://localhost:9200"
export ELASTICSEARCH_API_KEY="your_api_key"
# Or use username/password:
# export ELASTICSEARCH_USERNAME="elastic"
# export ELASTICSEARCH_PASSWORD="your_password"
export VERIFY_CERTS="false"

2. Connect to an MCP Client

CrowdSentinel works with any MCP-compatible AI agent. Choose your client below:

<details> <summary><b>Claude Code (CLI)</b></summary>

claude mcp add crowdsentinel \
  -e ELASTICSEARCH_HOSTS="https://localhost:9200" \
  -e ELASTICSEARCH_API_KEY="your_api_key" \
  -e VERIFY_CERTS="false" \
  -- uvx crowdsentinel-mcp-server

</details>

<details> <summary><b>Claude Desktop</b></summary>

Edit ~/.config/Claude/claude_desktop_config.json (Linux) or ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "crowdsentinel": {
      "command": "uvx",
      "args": ["crowdsentinel-mcp-server"],
      "env": {
        "ELASTICSEARCH_HOSTS": "https://localhost:9200",
        "ELASTICSEARCH_API_KEY": "your_api_key",
        "VERIFY_CERTS": "false"
      }
    }
  }
}

</details>

<details> <summary><b>VS Code Copilot</b></summary>

Create .vscode/mcp.json in your workspace:

{
  "servers": {
    "crowdsentinel": {
      "command": "uvx",
      "args": ["crowdsentinel-mcp-server"],
      "env": {
        "ELASTICSEARCH_HOSTS": "https://localhost:9200",
        "ELASTICSEARCH_API_KEY": "your_api_key",
        "VERIFY_CERTS": "false"
      }
    }
  }
}

Then enable MCP in VS Code settings: "chat.mcp.enabled": true </details>

<details> <summary><b>Cursor</b></summary>

Create or edit ~/.cursor/mcp.json:

{
  "mcpServers": {
    "crowdsentinel": {
      "command": "uvx",
      "args": ["crowdsentinel-mcp-server"],
      "env": {
        "ELASTICSEARCH_HOSTS": "https://localhost:9200",
        "ELASTICSEARCH_API_KEY": "your_api_key",
        "VERIFY_CERTS": "false"
      }
    }
  }
}

</details>

<details> <summary><b>Roo Code (VS Code Extension)</b></summary>

Create .roo/mcp.json in your workspace:

{
  "mcpServers": {
    "crowdsentinel": {
      "command": "uvx",
      "args": ["crowdsentinel-mcp-server"],
      "env": {
        "ELASTICSEARCH_HOSTS": "https://localhost:9200",
        "ELASTICSEARCH_API_KEY": "your_api_key",
        "VERIFY_CERTS": "false"
      }
    }
  }
}

Or configure via Roo Code settings panel: Settings > MCP Servers > Add Server. </details>

<details> <summary><b>5ire</b></summary>

In 5ire settings (v0.15.0+), add an MCP server with:

• Command: uvx
• Arguments: crowdsentinel-mcp-server
• Environment Variables:
• ELASTICSEARCH_HOSTS = https://localhost:9200
• ELASTICSEARCH_API_KEY = your_api_key
• VERIFY_CERTS = false

Note: 5ire v0.14.0 has known MCP compatibility issues. Use v0.15.0+ for reliable operation.

</details>

<details> <summary><b>Any MCP Client (Generic)</b></summary>

stdio transport (default — works with most clients): ``json { "mcpServers": { "crowdsentinel": { "command": "uvx", "args": ["crowdsentinel-mcp-server"], "env": { "ELASTICSEARCH_HOSTS": "https://localhost:9200", "ELASTICSEARCH_API_KEY": "your_api_key", "VERIFY_CERTS": "false" } } } } ``

SSE transport (for web-based clients): ```bash crowdsentinel-mcp-server --transport sse --port 8001

Connect to: http://localhost:8001/sse/

**HTTP transport** (for REST API clients):

crowdsentinel-mcp-server --transport streamable-http --port 8001

Connect to: http://localhost:8001/mcp/

</details>

### 3. Or use the CLI directly

Download rules and tools (one-time)

crowdsentinel setup

Check cluster health

crowdsentinel health

Hunt for threats

crowdsentinel hunt "powershell encoded" -i winlogbeat-*

Run detection rules

crowdsentinel rules -p windows --tactic credential_access crowdsentinel detect windows_builtin_win_alert_mimikatz_keywords_lucene -i winlogbeat-*

Analyse PCAP files

crowdsentinel pcap overview capture.pcap crowdsentinel pcap beaconing capture.pcap

Hunt EVTX logs with Chainsaw

crowdsentinel chainsaw hunt /path/to/evtx/ --sigma-rules /path/to/sigma/ ```

---

<a name="cli-usage"></a>

CLI Usage

CrowdSentinel provides a full CLI for threat hunting from the terminal:

pip install crowdsentinel-mcp-server
crowdsentinel setup    # Download rules, Chainsaw, Sigma (one-time)
crowdsentinel --help

Available Commands

| Command | Description | Example | |:--------|:------------|:--------| | setup | Download detection rules, Chainsaw, and Sigma rules | crowdsentinel setup | | health | Show cluster health | crowdsentinel health | | indices | List all indices | crowdsentinel indices | | hunt | IR-focused threat hunt with IoC extraction | crowdsentinel hunt "powershell" -i winlogbeat- | | eql | Execute an EQL query | crowdsentinel eql "process where process.name == 'cmd.exe'" -i winlogbeat- | | esql | Execute an ES\|QL query | crowdsentinel esql "FROM logs- \| LIMIT 10" | | detect | Execute a detection rule by ID | crowdsentinel detect win_susp_logon -i winlogbeat- | | rules | List available detection rules | crowdsentinel rules -p windows --tactic credential_access --type eql | | schema | Detect schema for an index pattern | crowdsentinel schema -i winlogbeat- | | ioc | Hunt for a specific Indicator of Compromise | crowdsentinel ioc 203.0.113.42 --type ip -i winlogbeat- | | analyse | Analyse search results from stdin (JSON) | cat results.json \| crowdsentinel analyse -c "context" | | analyse --mcp | AI agent analysis using all 139 MCP tools | crowdsentinel hunt "query" \| crowdsentinel analyse --mcp -c "context" | | auth | Manage LLM authentication for agent mode | crowdsentinel auth login | | pcap | Analyse PCAP files (overview, beaconing, lateral movement) | crowdsentinel pcap beaconing capture.pcap | | chainsaw | Hunt EVTX logs with Chainsaw and Sigma rules | crowdsentinel chainsaw hunt /path/to/evtx/ | | doctor | Validate connector configuration and readiness (offline — no external calls) | crowdsentinel doctor |

Output Formats

All commands support --output/-o with three formats:

crowdsentinel hunt "failed login" -i winlogbeat-* -o json     # Structured JSON (default)
crowdsentinel hunt "failed login" -i winlogbeat-* -o table    # Human-readable table
crowdsentinel hunt "failed login" -i winlogbeat-* -o summary  # Condensed summary

Agent Mode (--mcp)

The analyse --mcp flag replaces deterministic analysis with an AI agent that autonomously uses all 139 MCP tools to investigate. The agent follows the 4-phase IR methodology: hunt, analyse, correlate, report.

Authentication:

# Option 1: Browser sign-in (ChatGPT subscription — no API billing)
crowdsentinel auth login

# Option 2: Anthropic (setup-token or API key)
crowdsentinel auth login --provider anthropic

# Option 3: Environment variable
export ANTHROPIC_API_KEY="sk-ant-..."   # or OPENAI_API_KEY

# Option 4: Local models (Ollama, vLLM — free)
crowdsentinel analyse --mcp --model-url http://localhost:11434/v1 --model llama3.1

# Check auth status
crowdsentinel auth status

Agent flags:

| Flag | Default | Description | |:-----|:--------|:------------| | --mcp | off | Enable AI agent with MCP tools | | --mcp-server NAME:CMD | none | Add external MCP server (e.g., VirusTotal) | | --model | auto-detect | LLM model to use | | --model-url | none | OpenAI-compatible API endpoint | | --max-steps | 30 | Maximum tool calls | | --timeout | 300 | Maximum seconds |

Pipeline Examples

Deterministic analysis (no API key needed):

# Hunt then analyse
crowdsentinel hunt "powershell encoded" -i winlogbeat-* -o json | \
  crowdsentinel analyse -c "Encoded PowerShell commands" -o summary

# Investigate failed authentication attempts
crowdsentinel hunt "event.code:4625" -i winlogbeat-* -o json | \
  crowdsentinel analyse -c "Failed login brute force investigation" -o summary

# Triage process execution and privilege escalation
crowdsentinel hunt "event.code:4688 OR event.code:4672 OR event.code:1" -i winlogbeat-* -o json | \
  crowdsentinel analyse -c "Process execution and privilege escalation" -o summary

AI agent investigation (requires auth):

# Credential dumping investigation — agent hunts, analyses kill chain, checks adjacent stages
crowdsentinel hunt "mimikatz OR lsass OR procdump" -i winlogbeat-* -o json | \
  crowdsentinel analyse --mcp -c "Credential dumping tools investigation" --max-steps 15 -o summary

# Encoded PowerShell — full IR workflow with kill chain and adjacent stage hunting
crowdsentinel hunt "powershell -enc OR FromBase64String" -i winlogbeat-* -o json | \
  crowdsentinel analyse --mcp -c "Full IR workflow: encoded PowerShell" --max-steps 30 -o table

# Process execution with detection rules
crowdsentinel hunt "event.code:4688" -i winlogbeat-* -o json | \
  crowdsentinel analyse --mcp -c "Execute detection rules against process creation" --max-steps 20 -o summary

# PCAP beaconing — agent generates IoCs and maps to kill chain
crowdsentinel pcap beaconing capture.pcap -o json | \
  crowdsentinel analyse --mcp -c "Investigate beaconing for C2 infrastructure" --max-steps 10 -o summary

# Anti-forensics investigation
crowdsentinel hunt "event.code:1102" -i winlogbeat-* -o json | \
  crowdsentinel analyse --mcp -c "Security log cleared - anti-forensics" --max-steps 10 -o summary

# With external MCP server (e.g., VirusTotal)
crowdsentinel hunt "powershell" -i winlogbeat-* -o json | \
  crowdsentinel analyse --mcp --mcp-server "vt:uvx virustotal-mcp-server" \
  -c "Check IoCs against VirusTotal" -o summary

---

<a name="key-features"></a>

Key Features

<table> <tr> <td width="50%">

139 MCP Tools

Threat hunting, detection rules, forensics, endpoint collection, network analysis, cross-correlation, command deobfuscation, and IoC enrichment — all accessible via natural language

6,060 Detection Rules

Pre-built Lucene, EQL & ES|QL rules with automatic MITRE ATT&CK mapping

Investigation State

Persistent IoC tracking across tools and sessions with cross-source correlation and FIFO storage

</td> <td width="50%">

4 Security Frameworks

• Cyber Kill Chain (7 stages)
• Pyramid of Pain (6 levels)
• Diamond Model (4 vertices)
• MITRE ATT&CK (automatic mapping)

5 Data Sources + Threat Intel

• Elasticsearch / OpenSearch (SIEM)
• Velociraptor (live endpoint forensics)
• osquery (local host forensics)
• EVTX logs (Chainsaw + Sigma)
• PCAP files (Wireshark/TShark)
• IoC enrichment (Shodan, VirusTotal, AbuseIPDB, ThreatFox)
• MISP (export events, search attributes)

</td> </tr> </table>

---

<a name="architecture"></a>

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                 LLM Client / Claude Code CLI                    │
└─────────────────────────────┬───────────────────────────────────┘
                              │ MCP Protocol (stdio/SSE/HTTP)
                              │ HTTP: Origin validation (anti DNS-rebinding)
                              │       + transport-level audit logging
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    CrowdSentinel MCP Server                     │
│  ┌───────────────┐ ┌───────────────┐ ┌───────────────────────┐  │
│  │  139 Tools    │ │ 6,060 Rules   │ │ Security Frameworks   │  │
│  │ - Hunting     │ │ - Lucene      │ │ - Cyber Kill Chain    │  │
│  │ - Detection   │ │ - EQL         │ │ - Pyramid of Pain     │  │
│  │ - Forensics   │ │ - ES|QL       │ │ - Diamond Model       │  │
│  │ - Endpoint    │ │               │ │ - MITRE ATT&CK        │  │
│  │ - Network     │ ├───────────────┤ │                       │  │
│  │ - Enrichment  │ │ Schema        │ │ 9 MCP Resources       │  │
│  │ - Correlation │ │ Registry      │ │ (DFIR knowledge base) │  │
│  │ - Deobfuscate │ │ (data-driven) │ │                       │  │
│  └───────────────┘ └───────────────┘ └───────────────────────┘  │
│  ┌─────────────────────────────────────────────────────────────┐│
│  │              Investigation State (Persistent)               ││
│  │    Cross-source IoC sharing, auto-capture, STIX 2.1 export  ││
│  └─────────────────────────────────────────────────────────────┘│
└─────┬──────────┬──────────┬──────────────┬─────────────┬────────┘
      │          │          │              │             │
      ▼          ▼          ▼              ▼             ▼
┌───────────┐ ┌──────────┐ ┌────────┐ ┌───────────┐ ┌───────────┐
│Elastic-   │ │Velocirap-│ │osquery │ │ Chainsaw  │ │ Wireshark │
│search/    │ │tor       │ │(local  │ │(EVTX/     │ │  (PCAP)   │
│OpenSearch │ │(EDR/DFIR)│ │ host)  │ │ Sigma)    │ │ (Network) │
│  (SIEM)   │ │(Endpoint)│ │        │ │(Offline)  │ │           │
└───────────┘ └──────────┘ └────────┘ └───────────┘ └───────────┘
                          │
  ┌───────────────────────┼───────────────────────┐
  ▼                       ▼                       ▼
┌───────────────┐ ┌───────────────┐ ┌───────────────────────────┐
│ Shodan        │ │  VirusTotal   │ │  AbuseIPDB + ThreatFox    │
│ InternetDB    │ │     (v3)      │ │  + MISP (export/search)   │
│  (free/no key)│ │  (free tier)  │ │  (free tier)              │
└───────────────┘ └───────────────┘ └───────────────────────────┘
                          │
                          ▼ (Roadmap)
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│    Splunk     │ │     Wazuh     │ │     Zeek      │
│               │ │  (EDR/XDR)    │ │   (NSM/IDS)   │
└───────────────┘ └───────────────┘ └───────────────┘

---

What's Included

Tool Categories (139 Tools)

| Category | Tools | Description | |:---------|:-----:|:------------| | Elasticsearch Core | 19 | Index, document, cluster, alias, data stream, raw API operations | | Threat Hunting | 8 | Attack pattern detection, kill-chain stage hunting, timeframe hunts, anomaly detection | | EQL & ES\|QL Hunting | 11 | Event-sequence queries, ES\|QL pipes, saved hunt history, capability detection | | Asset Discovery & Schema Intelligence | 12 | Index discovery, data-driven schema detection (ECS, Sysmon, auditd, Zeek, CloudTrail…), smart search | | IoC Analysis & Deobfuscation | 6 | IoC extraction, encoded-command decoding, kill-chain mapping, investigation reports | | Detection Rules | 9 | 6,060 rule library — list, execute, validate, suggest, batch execution | | Investigation Prompts | 5 | Fast triage spine — 10 critical IR questions | | Chainsaw (EVTX) | 5 | Sigma rule hunting, iterative IoC discovery | | Wireshark (PCAP) | 11 | Network forensics, beaconing, lateral movement detection, traffic decoding | | Threat Intelligence | 5 | IoC enrichment (Shodan, VirusTotal, AbuseIPDB, ThreatFox) + MISP export/search | | Investigation State | 10 | Persistent IoCs, cross-tool sharing, STIX 2.1 export, reporting | | Velociraptor (Endpoint) | 25 | Live forensic artefact collection — processes, network, persistence, execution evidence, MFT, SRUM | | osquery (Local Host) | 4 | SQL-based local host forensics — processes, sockets, users, ad-hoc queries | | Cross-Source Correlation | 5 | SIEM ↔ endpoint pivots, IoC-driven endpoint hunts, unified timelines, time correlation | | Workflow Guidance & Status | 4 | Next-step guidance, progressive disclosure, system status, storage cleanup |

Velociraptor tools register when VELOCIRAPTOR_API_CONFIG is set; osquery tools register when a local osqueryi binary is present; the 10 high-risk write tools are hidden when DISABLE_HIGH_RISK_OPERATIONS=true.

Security Frameworks

| Framework | Purpose | |:----------|:--------| | Cyber Kill Chain | Hunt by attack stage (7 stages), predict adversary's next move | | Pyramid of Pain | Prioritise IoCs by difficulty for attackers to change (6 levels) | | Diamond Model | Map adversary, capability, infrastructure, victim relationships | | MITRE ATT&CK | Automatic technique mapping for all detections |

Detection Rules (6,060 Rules)

| Type | Count | Source | Description | |:-----|------:|:-------|:------------| | Lucene | 1,966 | Sigma-converted | Fast pattern matching queries | | EQL | 3,963 | Sigma-converted + Elastic | Event sequences and correlations | | ES\|QL | 131 | Elastic TOML rules | Pipe-based query language (ES 8.11+) |

Platforms: Windows, Linux, macOS, Cloud (AWS/Azure/GCP), Network, Identity

Log Sources: PowerShell, Sysmon, Security Events, Process Creation, Audit logs

Encoded-Command Deobfuscation

Real intrusions hide C2 addresses and URLs inside encodings the SIEM stores verbatim — most commonly PowerShell -EncodedCommand (base64 of UTF-16LE), but also FromBase64String, base64→gzip, bare base64, and hex blobs. CrowdSentinel decodes these layers automatically during IoC extraction and via the dedicated decode_command tool, so the inner indicators surface instead of staying buried:

• Zero extra dependencies — the decoder core uses only the Python standard library (base64/zlib/codecs/binascii)
• Optional long tail — install the deobf extra (pip install 'crowdsentinel-mcp-server[deobf]') to add chepy for XOR and exotic encodings; its absence degrades gracefully
• Provenance — every decode records its recipe (e.g. base64->utf-16le) for the IR report
• Safe by design — decode-only (nothing is executed), with depth and output-size bounds so a decompression bomb cannot hang the server

---

Configuration

Environment Variables

# Connection (required — choose one)
ELASTICSEARCH_HOSTS="https://localhost:9200"       # Self-hosted
# OR
ELASTICSEARCH_CLOUD_ID="deployment:base64..."      # Elastic Cloud

# Authentication — choose one (in priority order):
ELASTICSEARCH_BEARER_TOKEN="service_token_here"    # Service/bearer token
ELASTICSEARCH_API_KEY="your_api_key"               # API key (recommended)
ELASTICSEARCH_USERNAME="elastic"                   # Basic auth
ELASTICSEARCH_PASSWORD="your_password"

# TLS / Certificate verification
VERIFY_CERTS="true"                                # Verify against system CA bundle
# VERIFY_CERTS="/path/to/ca.crt"                   # Verify against custom CA certificate
# ELASTICSEARCH_CA_CERT="/path/to/ca.crt"          # Explicit CA certificate path
# ELASTICSEARCH_CLIENT_CERT="/path/to/client.crt"  # Client certificate (mTLS)
# ELASTICSEARCH_CLIENT_KEY="/path/to/client.key"   # Client private key (mTLS)

# Options
REQUEST_TIMEOUT="30"                               # Request timeout in seconds
DISABLE_HIGH_RISK_OPERATIONS="true"                # Block all write operations

# HTTP/SSE transport security (stdio unaffected)
# MCP_ALLOWED_ORIGINS="https://app.example.com"    # Extra allowed Origins (comma-separated); loopback always allowed

# Velociraptor endpoint forensics (optional — enables 25 extra tools)
# VELOCIRAPTOR_API_CONFIG="/path/to/api_client.yaml"

# Threat Intelligence (optional — Shodan InternetDB works without any key)
VIRUSTOTAL_API_KEY="your_vt_key"                   # Free: 500 lookups/day
ABUSEIPDB_API_KEY="your_abuse_key"                 # Free: 1,000 lookups/day
THREATFOX_API_KEY="your_tf_key"                    # Free: unlimited

# MISP Integration (optional — offline JSON export works without a server)
MISP_URL="https://misp.example.org"                # MISP instance URL
MISP_API_KEY="your_misp_key"                       # MISP API key (40-char hex)
# MISP_SSL_VERIFY="true"                           # Set "false" for self-signed (dev only)

Security Warning: Never use VERIFY_CERTS="false" or plain-text passwords in production. Use API keys or service tokens with TLS certificate verification enabled. For self-signed certificates, set ELASTICSEARCH_CA_CERT to your CA certificate path.

Production Configuration Examples

<details> <summary><b>Elastic Cloud</b></summary>

ELASTICSEARCH_CLOUD_ID="my-deployment:dXMtY2VudHJhbC0x..."
ELASTICSEARCH_API_KEY="your_cloud_api_key"
VERIFY_CERTS="true"

</details>

<details> <summary><b>Self-Hosted with Custom CA</b></summary>

ELASTICSEARCH_HOSTS="https://es-cluster.internal:9200"
ELASTICSEARCH_API_KEY="your_api_key"
ELASTICSEARCH_CA_CERT="/etc/elasticsearch/certs/ca.crt"
VERIFY_CERTS="true"

</details>

<details> <summary><b>Mutual TLS (mTLS)</b></summary>

ELASTICSEARCH_HOSTS="https://es-cluster.internal:9200"
ELASTICSEARCH_CA_CERT="/etc/elasticsearch/certs/ca.crt"
ELASTICSEARCH_CLIENT_CERT="/etc/elasticsearch/certs/client.crt"
ELASTICSEARCH_CLIENT_KEY="/etc/elasticsearch/certs/client.key"
VERIFY_CERTS="true"

</details>

<details> <summary><b>Development / Testing (insecure)</b></summary>

ELASTICSEARCH_HOSTS="http://localhost:9200"
ELASTICSEARCH_USERNAME="elastic"
ELASTICSEARCH_PASSWORD="test123"
VERIFY_CERTS="false"

This configuration is not suitable for production. Use API keys with TLS in production environments.

</details>

Transport Modes

| Mode | Use Case | Command | |:-----|:---------|:--------| | stdio (default) | MCP clients (Claude Code, etc.) | Auto-configured by setup.sh | | SSE | Web/streaming clients | --transport sse --port 8000 | | HTTP | REST API access | --transport streamable-http |

The HTTP transports are hardened at the transport layer:

• Origin validation (anti DNS-rebinding) — browser requests are rejected unless their Origin is loopback or listed in MCP_ALLOWED_ORIGINS (comma-separated; * alone disables the check). Requests without an Origin header — every non-browser MCP client — are unaffected.
• Audit logging — every HTTP exchange is logged at the transport boundary (JSON-RPC method, tool name, body size, path), including requests the tool layer never sees, such as unknown tools or protocol errors. Logs are metadata-only, memory-bounded, and sanitised against log forgery.

Disabling Write Operations

For read-only access (recommended for production):

DISABLE_HIGH_RISK_OPERATIONS="true"

This hides destructive tools: create_index, delete_index, index_document, delete_document, delete_by_query, create_data_stream, delete_data_stream, put_alias, delete_alias, general_api_request

---

<a name="usage-examples"></a>

Usage Examples

Example 1: Hunt for Brute Force Attacks

You: Hunt for brute force attacks in the last hour

CrowdSentinel: Searching for failed authentication attempts...
[Calls hunt_by_timeframe with attack_types=["brute_force"]]

Found 47 failed login attempts from 3 source IPs targeting the "admin" account.
Top sources:
- 192.168.1.105: 28 attempts
- 10.0.0.42: 15 attempts
- 172.16.0.8: 4 attempts

Example 2: Investigate a Suspicious Host

You: Show me all activity on host DESKTOP-ABC123 in the last 24 hours

CrowdSentinel: Generating a forensic timeline for that host...
[Calls get_host_activity_timeline]

Timeline shows:
- 09:15: User "jsmith" logged in (Event 4624)
- 09:17: PowerShell executed encoded command
- 09:18: New scheduled task created "WindowsUpdate"
- 09:22: Outbound connection to 203.0.113.42:443
- 09:25: LSASS memory access detected

Example 3: Kill Chain Analysis

You: We found C2 beaconing. What should we hunt for next?

CrowdSentinel: Hunting adjacent kill chain stages...
[Calls hunt_adjacent_stages with current_stage="COMMAND_AND_CONTROL"]

Looking backwards (Installation stage):
- Found scheduled task persistence on 3 hosts
- Registry Run key modifications detected

Looking forwards (Actions on Objectives):
- Lateral movement to 2 additional hosts via SMB
- Credential dumping attempts detected

Example 4: Network Traffic Analysis

You: Analyse this PCAP for C2 beaconing

CrowdSentinel: Checking for regular connection intervals...
[Calls detect_beaconing on the PCAP file]

Detected beaconing pattern:
- Host: 192.168.1.50 → 203.0.113.100:443
- Interval: 60 seconds (±2s jitter)
- Duration: 4 hours
- Confidence: HIGH

Recommend blocking 203.0.113.100 and investigating 192.168.1.50.

Example 5: CLI Threat Hunt

# Hunt for encoded PowerShell
crowdsentinel hunt "powershell -enc" -i winlogbeat-* --timeframe 1440 -o json

# Pipe results to analysis
crowdsentinel hunt "event.code:4625" -i winlogbeat-* -o json | \
  crowdsentinel analyse -c "Failed authentication investigation"

# Search detection rules for lateral movement
crowdsentinel rules --tactic lateral_movement -p windows

---

Compatibility

| Package | Backend | Install | |:--------|:--------|:--------| | crowdsentinel-mcp-server | Elasticsearch 8.x (default) | pip install crowdsentinel-mcp-server | | crowdsentinel-mcp-server-es7 | Elasticsearch 7.x | pip install crowdsentinel-mcp-server-es7 | | crowdsentinel-mcp-server-es9 | Elasticsearch 9.x | pip install crowdsentinel-mcp-server-es9 | | opensearch-mcp-server | OpenSearch 1.x, 2.x, 3.x | pip install opensearch-mcp-server |

---

For Developers

<details> <summary><b>Project Structure</b></summary>

crowdsentinel-mcp-server/
├── src/
│   ├── server.py                 # MCP server entry point
│   ├── version.py                # Version constant
│   ├── risk_config.py            # Write operation controls
│   ├── transport_security.py     # Origin validation for HTTP transports (anti DNS-rebinding)
│   ├── transport_audit.py        # Transport-level audit logging (every HTTP exchange)
│   │
│   ├── cli/                      # Standalone CLI
│   │   └── main.py               # CLI entry point (argparse) — 15 commands incl. doctor
│   │
│   ├── agent/                    # `analyse --mcp` AI agent mode (multi-provider LLM auth)
│   ├── config/                   # Connector configuration
│   │
│   ├── clients/                  # Backend logic layer
│   │   ├── base.py               # Base client, authentication
│   │   ├── exceptions.py         # Exception handling decorators
│   │   └── common/
│   │       ├── client.py         # Unified SearchClient (multiple inheritance)
│   │       ├── threat_hunting.py # Threat hunting queries
│   │       ├── ioc_analysis.py   # IoC extraction & analysis
│   │       ├── deobfuscate.py    # Encoded-command decoding (stdlib core + optional chepy)
│   │       ├── cyber_kill_chain.py # Kill chain logic
│   │       ├── rule_loader.py    # Detection rule loading
│   │       ├── chainsaw_client.py # EVTX/Sigma integration
│   │       ├── osquery_client.py # Local osquery execution (sqlglot-validated SQL)
│   │       ├── misp_client.py    # MISP export/search
│   │       ├── threat_intel.py   # Shodan/VirusTotal/AbuseIPDB/ThreatFox enrichment
│   │       └── schemas/          # Data-driven schema registry
│   │           ├── ecs.py, sysmon.py, windows_security.py, auditd.py
│   │           └── defs/         # JSON schema defs (zeek, packetbeat, aws_cloudtrail, …)
│   │
│   ├── tools/                    # MCP tool interfaces (25 modules, 139 tools)
│   │   ├── register.py           # Dynamic tool registration
│   │   ├── threat_hunting.py     # Hunting tool definitions
│   │   ├── rule_management.py    # Rule management tools
│   │   ├── velociraptor_tools.py # Live endpoint forensics (25 tools)
│   │   ├── osquery_tools.py      # Local host forensics (4 tools)
│   │   ├── cross_correlation.py  # SIEM ↔ endpoint correlation (5 tools)
│   │   ├── chainsaw_hunting.py   # Chainsaw tools
│   │   ├── wireshark_tools.py    # Network analysis tools
│   │   ├── threat_intel.py       # Enrichment + MISP tools
│   │   └── investigation_state_tools.py # State management tools
│   │
│   ├── storage/                  # Persistent investigation state
│   │   ├── investigation_state.py # Core state management
│   │   ├── storage_manager.py    # File system storage (8GB FIFO)
│   │   └── models.py             # Pydantic models (IoC, Investigation)
│   │
│   ├── utils/                    # Shared utilities
│   │
│   └── wireshark/                # Network traffic analysis
│       ├── core/                 # TShark execution, PCAP parsing
│       ├── hunting/              # Beaconing, lateral movement, IoC hunting
│       ├── baseline/             # Traffic baseline creation
│       ├── extraction/           # File carving from traffic
│       └── reporting/            # NCSC-style reports, timelines
│
├── rules/                        # 6,060 detection rules (EQL + Lucene)
├── chainsaw/                     # Chainsaw binary + 3,000+ Sigma rules
├── skills/                       # Claude Code agent skills
├── Makefile                      # Test pyramid + release automation
└── tests/                        # Tiered test suite (unit / integration / api / e2e / system)

</details>

<details> <summary><b>Design Patterns</b></summary>

| Pattern | Usage | |:--------|:------| | Multiple Inheritance | SearchClient composes all specialised clients | | Decorator | Exception handling via @handle_exceptions | | Factory | create_search_client() creates appropriate client | | Plugin Architecture | Tools registered dynamically via ToolsRegister | | Auto-Capture | Tool results automatically analysed for IoCs |

</details>

<details> <summary><b>Adding a New Tool</b></summary>

1. Create client method in src/clients/common/your_module.py:

class YourClient(SearchClientBase):
    def your_method(self, param: str) -> dict:
        # Implementation
        return results

1. Add to SearchClient in src/clients/common/client.py:

class SearchClient(YourClient, OtherClients, ...):
    pass

1. Create tool wrapper in src/tools/your_tools.py:

class YourTools:
    def __init__(self, client, mcp):
        self.client = client
        self.mcp = mcp

    def register_tools(self):
        @self.mcp.tool()
        def your_tool(param: str) -> str:
            """Tool description for LLM."""
            result = self.client.your_method(param)
            return json.dumps(result)

1. Register in server in src/server.py:

from src.tools.your_tools import YourTools

def _register_tools(self):
    # ... existing tools ...
    YourTools(self.client, self.mcp).register_tools()

</details>

<details> <summary><b>Running Tests</b></summary>

The test suite is a bottom-up pyramid (see tests/README.md): hermetic unit tests, then live tiers that spin up a real Elasticsearch (via testcontainers) and the real MCP server.

make test-unit          # fast hermetic loop (default developer command)
make coverage           # unit tier + coverage policy gate
make check-tests        # policy: every new function vs origin/main has a test
make test-integration   # real ES via testcontainers (Docker required)
make test-api           # + real MCP server subprocess over streamable-http
make test-e2e           # + real MCP client journey over the wire
make test-suite         # the whole pyramid, bottom-up, with a summary table

| Tier | What it proves | Backing services | |:-----|:---------------|:-----------------| | Unit | Function-level behaviour, fail-fast validation | None (hermetic) | | Integration | App ↔ Elasticsearch trust boundary (auth, TLS, seeded data) | Real ES via testcontainers | | API | MCP-over-HTTP contract (schemas, errors, Origin security, audit logs) | Real ES + real server subprocess | | E2E | Full analyst journey via a real MCP client | Real ES + real server + fastmcp.Client | | System | Full pipeline against your own range cluster | BYO Elasticsearch |

Live tiers are opt-in (--run-integration / --run-api / --run-e2e / --run-system), so plain pytest tests/ stays fast and hermetic.

</details>

<details> <summary><b>Local Testing Environment</b></summary>

# Start Elasticsearch
docker-compose -f docker-compose-elasticsearch.yml up -d

# Start OpenSearch
docker-compose -f docker-compose-opensearch.yml up -d

Default credentials (testing only):

• Elasticsearch: elastic / test123
• OpenSearch: admin / admin

</details>

---

Roadmap

| Feature | Status | Description | |:--------|:------:|:------------| | Velociraptor Integration | Done | 25 live endpoint forensics tools + 5 cross-correlation tools via the Velociraptor API | | osquery Integration | Done | SQL-based local host forensics (processes, sockets, users, ad-hoc queries) | | Encoded-Command Deobfuscation | Done | Recover IoCs hidden in base64/UTF-16LE/gzip/hex-encoded commands (optional chepy for the long tail) | | Threat Intel Enrichment | Done | IoC enrichment via Shodan InternetDB, VirusTotal, AbuseIPDB, ThreatFox + STIX 2.1 export | | MISP Integration | Done | Export investigation IoCs as MISP events, search MISP attributes (offline JSON export without a server) | | Zeek Integration | Partial | Zeek logs indexed into ES are hunted via the data-driven schema registry; native NSM integration planned | | Wazuh Integration | Planned | Open-source EDR/XDR — its indexer is OpenSearch-based, so CrowdSentinel's existing OpenSearch client can hunt Wazuh alerts and agent telemetry | | Splunk Integration | Planned | Add Splunk as a data source alongside Elasticsearch | | Sigma Rule Converter | Planned | Convert Sigma rules to native ES/Splunk queries | | Case Management | Planned | Export investigations to TheHive, JIRA | | Custom Rule Builder | Planned | Create detection rules via natural language |

See CHANGELOG.md for detailed version history.

---

Velociraptor Integration (Live Endpoint Forensics)

CrowdSentinel integrates with Velociraptor for live endpoint forensic artefact collection. When configured, 25 additional MCP tools and 5 cross-correlation tools become available.

Setup

# Install optional dependencies
pip install crowdsentinel-mcp-server[velociraptor]

# Set the Velociraptor API config path
export VELOCIRAPTOR_API_CONFIG="/path/to/api_client.yaml"

The API client config is generated from the Velociraptor server: ``bash velociraptor config api_client --name crowdsentinel --role administrator,api \ --config server.config.yaml /path/to/api_client.yaml ``

Available Tools

| Category | Tools | Use Case | |:---------|:------|:---------| | Discovery | velociraptor_client_info, velociraptor_list_clients, velociraptor_list_artifacts | Resolve hostnames, enumerate enrolled endpoints, discover available artefacts | | Generic Collection | velociraptor_collect_artifact, velociraptor_get_collection_results | Collect any Velociraptor artefact and fetch its results | | Live State | velociraptor_pslist, velociraptor_netstat, velociraptor_users, velociraptor_groups, velociraptor_mounts | Running processes, active network connections, accounts, groups, mounted volumes | | Execution Evidence | velociraptor_prefetch, velociraptor_amcache, velociraptor_shimcache, velociraptor_userassist, velociraptor_bam, velociraptor_srum | Forensic proof of programme execution and resource usage | | Persistence | velociraptor_services, velociraptor_scheduled_tasks, velociraptor_autoruns | Service, scheduled task, and autorun persistence mechanisms | | User Activity | velociraptor_shellbags, velociraptor_recentdocs, velociraptor_lnk, velociraptor_evidence_of_download | User browsing, document access, link files, file downloads | | Filesystem & Logs | velociraptor_ntfs_mft, velociraptor_evtx | MFT search by name/path/timestamp, remote EVTX queries | | Cross-Correlation | correlate_siem_with_endpoint, endpoint_to_siem_pivot, endpoint_hunt_from_ioc, correlate_by_time, build_unified_timeline | Validate SIEM findings on endpoints, pivot from endpoint IoCs to fleet-wide SIEM searches, unified timelines |

Cross-Source IoC Sharing

IoCs extracted from Velociraptor artefacts are automatically captured into the active investigation alongside SIEM, Chainsaw, and Wireshark findings. Use get_shared_iocs to retrieve the combined indicator set for cross-source correlation.

SIEM (Elasticsearch)          Endpoint (Velociraptor)
       │                              │
  hunt_by_timeframe()         velociraptor_pslist()
       │                              │
  auto_capture ──────► Investigation State ◄────── auto_capture
                              │
                     get_shared_iocs()

DFIR Knowledge Resources

9 MCP resources provide structured investigation reference data directly to connected AI agents:

| Resource | Content | |:---------|:--------| | crowdsentinel://investigation-workflow | Mandatory 4-phase IR workflow | | crowdsentinel://ioc-reference | IoC types ranked by Pyramid of Pain | | crowdsentinel://ioc-reference/data | Machine-readable IoC reference data | | crowdsentinel://cross-correlation-playbooks | 5 investigation playbooks (suspicious process, brute force, lateral movement, persistence, exfiltration) | | crowdsentinel://velociraptor-guide | Artefact reference tables and "found X in SIEM → check Y on endpoint" decision tree | | crowdsentinel://data-sources | All data source capabilities and investigation decision matrix | | crowdsentinel://tool-recommendations | Which tool to reach for at each investigation step | | crowdsentinel://schemas | Supported log-source schemas (ECS, Sysmon, Windows Security, auditd, Zeek, …) | | crowdsentinel://schemas/list | Machine-readable schema listing |

---

<a name="documentation"></a>

Documentation

User Guides

| Document | Description | |:---------|:------------| | FIRST_TIME_SETUP.md | Detailed first-time setup instructions | | HOW_TO_USE.md | Comprehensive usage guide | | QUICK_START.md | 5-minute quick start | | TRANSPORT_MODES.md | stdio, SSE, HTTP configuration |

Feature Guides

| Document | Description | |:---------|:------------| | THREAT_HUNTING_GUIDE.md | Threat hunting workflows | | DETECTION_RULES_GUIDE.md | Using 6,060 detection rules | | CYBER_KILL_CHAIN_GUIDE.md | Kill chain analysis | | CHAINSAW_GUIDE.md | EVTX log analysis with Sigma | | INVESTIGATION_PROMPTS_GUIDE.md | Fast triage spine | | AI_AGENT_INTEGRATION.md | Workflow guidance for AI agents |

Developer Guides

| Document | Description | |:---------|:------------| | ARCHITECTURE.md | Detailed architecture documentation | | CONTRIBUTING.md | Contribution guidelines |

---

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

---

Licence

GNU General Public Licence v3.0 — See LICENSE for details.

---

Cyber Range

CrowdSentinel ships with a companion crowdsentinel-range — a fully ephemeral AWS lab that generates ground-truth-labeled attack telemetry so you can investigate real detections rather than synthetic data.

Current range (v1) — 2-host AWS lab

┌──────────────────────────── AWS VPC (private subnet) ────────────────────────────────┐
│                                                                                        │
│  ┌──────────────────────────────┐      ┌──────────────────────────────────────────┐  │
│  │   Linux SIEM (Ubuntu 22.04)  │      │   Windows Range Box (Server 2022)        │  │
│  │   Elasticsearch 8.x (TLS)   │◄─────│   Sysmon (olafhartong) + Winlogbeat 9    │  │
│  │   Auditbeat + Filebeat       │      │   Atomic Red Team + PurpleSharp          │  │
│  └──────────────────────────────┘      └──────────────────────────────────────────┘  │
│                         SSM VPC endpoints — no inbound ports open                     │
└───────────────────────────────────────┬────────────────────────────────────────────--┘
                                         │ SSM port-forward :19200
                              ┌──────────▼──────────────────────┐
                              │  ./range-mcp.sh → CrowdSentinel │
                              │  MCP Server → Claude Code       │
                              └─────────────────────────────────┘

3 commands to go from zero to hunting: ```bash cd crowdsentinel-range && make up && make attack-windows cd .. && ./range-mcp.sh

then in Claude Code: /mcp → "investigate potential intrusions on the windows machine"

> Demo: [73,328 events triaged in ~40 seconds](https://github.com/thomasxm/crowdsentinel-range/blob/main/docs/demo-windows-triage-investigation.md) — 2 shellcode payloads decoded, C2 IP extracted from raw bytes, full IR report with 19 MITRE techniques.

---

### Future range (v2) — enterprise AD environment

The next range generation targets **full Active Directory kill-chain coverage**: initial access → lateral movement → domain compromise, with a network security monitor layer, runtime security instrumentation, centralised vulnerability management, and deception infrastructure. Inspired by [DetectionLab](https://github.com/clong/DetectionLab), [Ludus](https://docs.ludus.cloud/), and [SimuLand](https://github.com/Azure/SimuLand).

┌───────────────────────────────────── AWS / Multi-VPC ───────────────────────────────────────────────────┐ │ │ │ ┌──── Attacker Subnet ────┐ ┌───────────── Corporate Subnet ──────────────────────────────────────┐ │ │ │ │ │ │ │ │ │ Kali Linux (C2 server) │ │ ┌─────────────────┐ ┌──────────────────┐ ┌──────────────────┐ │ │ │ │ Caldera + Metasploit │ │ │ Domain │ │ Windows 11 │ │ Windows 11 │ │ │ │ │ Covenant C2 │──▶│ │ Controller │ │ Workstation 1 │ │ Workstation 2 │ │ │ │ │ │ │ │ (AD DS, DNS) │ │ (domain-joined) │ │ (domain-joined) │ │ │ │ └──────────────────────────┘ │ └─────────────────┘ └──────────────────┘ └──────────────────┘ │ │ │ │ ┌──────────────────────────────────────────────────────────────┐ │ │ │ ┌──── DMZ Subnet ─────────┐ │ │ Linux File/Web Server (IIS / Apache, SMB share) │ │ │ │ │ │ │ └──────────────────────────────────────────────────────────────┘ │ │ │ │ Nginx reverse proxy │ └───────────────────────────────────────────────────────────────────┘ │ │ │ Simulated internet SVC │ │ │ └──────────────────────────┘ ┌───────────── Management / SIEM Subnet ──────────────────────────┐ │ │ │ │ │ │ │ ┌─────────────────────┐ ┌───────────────────────────────┐ │ │ │ │ │ Elasticsearch + │ │ Zeek / Suricata │ │ │ │ │ │ Kibana (SIEM) │ │ (NSM — full packet + alerts) │ │ │ │ │ └─────────────────────┘ └───────────────────────────────┘ │ │ │ │ ┌─────────────────────┐ ┌───────────────────────────────┐ │ │ │ │ │ Velociraptor │ │ Falco (runtime security) │ │ │ │ │ │ (live EDR/forensics)│ │ eBPF probe (CO-RE, default) │ │ │ │ │ └─────────────────────┘ │ + kernel module fallback │ │ │ │ │ ┌─────────────────────┐ └───────────────────────────────┘ │ │ │ │ │ DefectDojo │ ┌───────────────────────────────┐ │ │ │ │ │ (vuln management) │ │ Honeypots + Canary tokens │ │ │ │ │ │ Falco·Zeek·scanners │ │ (deception layer) │ │ │ │ │ └─────────────────────┘ └───────────────────────────────┘ │ │ │ │ ┌─────────────────────────────────────────────────────────┐ │ │ │ │ │ Grafana — centralized dashboards + alerting │ │ │ │ │ │ ES · Falco · Suricata · Velociraptor · DefectDojo │ │ │ │ │ └─────────────────────────────────────────────────────────┘ │ │ │ └───────────────────────────────────────────────────────────────┘ │ │ VyOS router — inter-subnet ACLs + egress filter │ └───────────────────────────────────────────────────────────────────────────────────────────────────────┘ │ SSM / VPN CrowdSentinel MCP + Claude Code ```

v2 target capabilities:

| Capability | v1 (current) | v2 (planned) | |------------|-------------|--------------| | Hosts | 2 (Linux SIEM + Windows) | 7–10 (DC + 2× WS + Linux + NSM + attacker + SIEM) | | Network segments | 1 (private subnet) | 4 (attacker / corp / DMZ / management) | | Active Directory | — | Full AD DS domain + GPO + Kerberos | | Lateral movement coverage | Limited | Pass-the-Hash, Kerberoasting, DCSync | | Network detection | — | Zeek (NSM) + Suricata IDS | | Runtime security | — | Falco; modern eBPF probe (CO-RE, bundled, default) + kernel module fallback; syscall-level host + container detection | | Endpoint forensics | — | Velociraptor live triage | | Vuln management | — | DefectDojo; aggregates Falco alerts, Zeek/Suricata findings, scanner results (OpenVAS, Trivy) into unified tracking + remediation workflow | | Dashboards | — | Grafana; centralised metrics + alert visualisation across ES, Falco, Suricata, Velociraptor, and DefectDojo | | Deception layer | — | Honeypot + canary tokens | | C2 infrastructure | ART atomics only | Caldera + Metasploit + Covenant | | Attack scenarios | Windows atomics + EVTX replay | Full kill-chain: initial access → domain compromise | | CrowdSentinel coverage | ES hunt + IR | ES + Zeek/Suricata + Falco + Velociraptor cross-source correlation; DefectDojo/Grafana as upstream context |

Design references: DetectionLab (Sysmon+WEF+Splunk convergence), Ludus (modular Proxmox/Ansible, VLAN segmentation), SimuLand (multi-domain AD forest, cloud-native), SANS FOR508 (artifact-first DFIR workflow), Falco (CO-RE eBPF probe architecture, kernel module fallback).

Acknowledgements

CrowdSentinel stands on the shoulders of outstanding open-source projects. Thank you to all of their maintainers and contributors.

Protocol & server framework

• Model Context Protocol — the protocol connecting LLMs to tools, by Anthropic
• FastMCP — the Python MCP server framework this project is built on

Analysis engines & forensic tools

• Chainsaw — EVTX log analyser by WithSecure Labs
• Velociraptor — live endpoint forensics and DFIR platform by Velocidex/Rapid7
• osquery — SQL-powered host instrumentation by the osquery Foundation
• Wireshark / TShark — network protocol analysis by the Wireshark Foundation
• Chepy — CyberChef-like decoding library powering optional long-tail deobfuscation
• sqlglot — SQL parser used to safely validate osquery queries

Detection content

• Sigma / SigmaHQ — the community detection rule standard and rule corpus
• Elastic detection-rules — Elastic's prebuilt EQL/ES|QL detection rules
• Community-contributed Sigma and custom rules

Data platforms & threat intelligence

• Elasticsearch and OpenSearch Python clients
• MISP / PyMISP — open-source threat intelligence platform and client
• stix2 — OASIS STIX 2.1 library for structured IoC export
• Shodan InternetDB, VirusTotal, AbuseIPDB, and abuse.ch ThreatFox — IoC enrichment services with free tiers

Development & testing

• testcontainers-python — real Elasticsearch containers for the live test tiers
• pytest, Ruff, Bandit — test runner, linting, and SAST

Methodology frameworks

• Cyber Kill Chain (Lockheed Martin), Pyramid of Pain (David J. Bianco), Diamond Model (Caltagirone, Pendergast & Betz), MITRE ATT&CK

---

<div align="center">

Made for the security community by medjedtxm

 

</div>