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CrowdSentinel MCP Server

by thomasxm

CrowdSentinel MCP Server

AI-Powered Threat Hunting & Incident Response Framework

PyPI PyPI Downloads Python License MCP Tools Rules

Install from PyPI

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

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.


Demo

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


Related MCP server: bhe_mcp

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)

  • Velociraptor Endpoint Forensics — 20 MCP tools for live artefact collection (processes, network, persistence, execution evidence, NTFS MFT) with automatic IoC extraction

  • 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


Installation

# 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

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

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

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.


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:

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

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"
      }
    }
  }
}

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

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"
      }
    }
  }
}

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.

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.

stdio transport (default — works with most clients):

{
  "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):

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/

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/

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 97 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/

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 97 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

Key Features

125+ MCP Tools

Threat hunting, detection rules, forensics, endpoint collection, network analysis, cross-correlation, 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

4 Security Frameworks

  • Cyber Kill Chain (7 stages)

  • Pyramid of Pain (6 levels)

  • Diamond Model (4 vertices)

  • MITRE ATT&CK (automatic mapping)

4 Data Sources + Threat Intel

  • Elasticsearch / OpenSearch (SIEM)

  • Velociraptor (live endpoint forensics)

  • EVTX logs (Chainsaw + Sigma)

  • PCAP files (Wireshark/TShark)

  • IoC enrichment (Shodan, VirusTotal, AbuseIPDB, ThreatFox)


Architecture

┌─────────────────────────────────────────────────────────────────┐
│                 LLM Client / Claude Code CLI                    │
└─────────────────────────────┬───────────────────────────────────┘
                              │ MCP Protocol (stdio/SSE/HTTP)
                              ▼
┌─────────────────────────────────────────────────────────────────┐
│                    CrowdSentinel MCP Server                      │
│  ┌───────────────┐ ┌───────────────┐ ┌───────────────────────┐  │
│  │  125+ Tools   │ │ 6,060 Rules   │ │ Security Frameworks   │  │
│  │ - Hunting     │ │ - Lucene      │ │ - Cyber Kill Chain    │  │
│  │ - Detection   │ │ - EQL         │ │ - Pyramid of Pain     │  │
│  │ - Forensics   │ │ - Sigma       │ │ - Diamond Model       │  │
│  │ - Endpoint    │ │               │ │ - MITRE ATT&CK        │  │
│  │ - Network     │ │               │ │                       │  │
│  │ - Enrichment  │ │               │ │ 9 MCP Resources       │  │
│  │ - Correlation │ │               │ │ (DFIR knowledge base) │  │
│  └───────────────┘ └───────────────┘ └───────────────────────┘  │
│  ┌─────────────────────────────────────────────────────────────┐│
│  │              Investigation State (Persistent)                ││
│  │    Cross-source IoC sharing, auto-capture, STIX 2.1 export  ││
│  └─────────────────────────────────────────────────────────────┘│
└──────────┬──────────────┬───────────────────┬───────────────────┘
           │              │                   │
  ┌────────┼──────────────┼───────────────┐   │
  ▼        ▼              ▼               ▼   ▼
┌──────────────┐ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│Elasticsearch │ │ Velociraptor  │ │   Chainsaw    │ │   Wireshark   │
│ /OpenSearch  │ │  (EDR/DFIR)   │ │  (EVTX/Sigma) │ │    (PCAP)     │
│   (SIEM)     │ │  (Endpoint)   │ │  (Offline)    │ │  (Network)    │
└──────────────┘ └───────────────┘ └───────────────┘ └───────────────┘
                          │
  ┌───────────────────────┼───────────────────────┐
  ▼                       ▼                       ▼
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│ Shodan        │ │  VirusTotal   │ │  AbuseIPDB    │
│ InternetDB    │ │     (v3)      │ │  + ThreatFox  │
│  (free/no key)│ │  (free tier)  │ │  (free tier)  │
└───────────────┘ └───────────────┘ └───────────────┘
                          │
                          ▼ (Roadmap)
┌───────────────┐ ┌───────────────┐ ┌───────────────┐
│    Splunk     │ │  Carbon Black │ │     Zeek      │
│               │ │  (EDR)        │ │   (NSM/IDS)   │
└───────────────┘ └───────────────┘ └───────────────┘

What's Included

Tool Categories (84 Tools)

Category

Tools

Description

Elasticsearch Core

18

Index, document, cluster, alias, data stream operations

Threat Hunting

12

Attack pattern detection, IoC hunting, timeline analysis

Detection Rules

9

6,060 rule library — list, execute, validate, suggest

Kill Chain Analysis

5

Stage hunting, progression tracking, adjacent stage prediction

Investigation Prompts

5

Fast triage spine — 10 critical IR questions

Chainsaw (EVTX)

6

Sigma rule hunting, iterative IoC discovery

Wireshark (PCAP)

11

Network forensics, beaconing, lateral movement detection

Threat Intelligence

5

IoC enrichment (Shodan, VirusTotal, AbuseIPDB, ThreatFox) + MISP export/search

Investigation State

13

Persistent IoCs, cross-tool sharing, STIX 2.1 export, reporting

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


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

# 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

ELASTICSEARCH_CLOUD_ID="my-deployment:dXMtY2VudHJhbC0x..."
ELASTICSEARCH_API_KEY="your_cloud_api_key"
VERIFY_CERTS="true"
ELASTICSEARCH_HOSTS="https://es-cluster.internal:9200"
ELASTICSEARCH_API_KEY="your_api_key"
ELASTICSEARCH_CA_CERT="/etc/elasticsearch/certs/ca.crt"
VERIFY_CERTS="true"
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"
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.

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

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


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

crowdsentinel-mcp-server/
├── src/
│   ├── server.py                 # MCP server entry point
│   ├── version.py                # Version constant
│   ├── risk_config.py            # Write operation controls
│   │
│   ├── cli/                      # Standalone CLI
│   │   └── main.py               # CLI entry point (argparse)
│   │
│   ├── 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
│   │       ├── cyber_kill_chain.py # Kill chain logic
│   │       ├── rule_loader.py    # Detection rule loading
│   │       └── chainsaw_client.py # EVTX/Sigma integration
│   │
│   ├── tools/                    # MCP tool interfaces (thin wrappers)
│   │   ├── register.py           # Dynamic tool registration
│   │   ├── threat_hunting.py     # Hunting tool definitions
│   │   ├── rule_management.py    # Rule management tools
│   │   ├── chainsaw_hunting.py   # Chainsaw tools
│   │   ├── wireshark_tools.py    # Network analysis 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)
│   │
│   └── 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
└── tests/                        # Test suites

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

  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()
# All tests
uv run pytest

# Specific module
uv run pytest tests/test_investigation_state.py

# With coverage
uv run pytest --cov=src
# 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


Roadmap

Feature

Status

Description

Velociraptor Integration

Planned

EDR/DFIR artifact collection and live response via Velociraptor API

Zeek Integration

Planned

Network security monitoring — parse Zeek logs (conn, dns, http, ssl, x509) for threat hunting

Splunk Integration

Planned

Add Splunk as a data source alongside Elasticsearch

Sigma Rule Converter

Planned

Convert Sigma rules to native ES/Splunk queries

Threat Intel Enrichment

Done

IoC enrichment via Shodan InternetDB, VirusTotal, AbuseIPDB, ThreatFox + STIX 2.1 export

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, 20 additional MCP tools and 3 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:

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_artifacts

Resolve hostnames, discover available artefacts

Live State

velociraptor_pslist, velociraptor_netstat, velociraptor_users

Running processes, active network connections, user accounts

Execution Evidence

velociraptor_prefetch, velociraptor_amcache, velociraptor_shimcache, velociraptor_userassist, velociraptor_bam

Forensic proof of programme execution

Persistence

velociraptor_services, velociraptor_scheduled_tasks

Service and scheduled task persistence mechanisms

User Activity

velociraptor_shellbags, velociraptor_recentdocs, velociraptor_evidence_of_download

User browsing, document access, file downloads

Filesystem

velociraptor_ntfs_mft

MFT search for files by name, path, or timestamp

Cross-Correlation

correlate_siem_with_endpoint, endpoint_to_siem_pivot, build_unified_timeline

Validate SIEM findings on endpoints, pivot from endpoint IoCs to fleet-wide SIEM searches

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://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


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.


Acknowledgements

  • MCP Framework: Model Context Protocol by Anthropic

  • Chainsaw: EVTX log analyser by WithSecure Labs

  • Detection Rules: Community-contributed Sigma and custom rules

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


Made for the security community by medjedtxm

GitHub PyPI

A
license - permissive license
-
quality - not tested
-
maintenance - not tested

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