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Cyber Host Artificial Intelligence (C.H.A.I) - mcp server

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- launch.json
- settings.json
- tasks.json
- __init__.py
- ai_planner.py
- audit_logger.py
- execution_loop.py
- process_controller.py
- safety_policy.py
- session_manager.py
- - pentest.profile
- init_graph.sql
- init_sessions.sql
- __init__.py
- graph_db.py
- playbook_loader.py
- vector_search.py
- - __init__.py
  - amazon_bedrock.py
  - anthropic_claude.py
  - azure_openai.py
  - huggingface.py
  - openai_direct.py
  - openrouter.py
- __init__.py
- base_provider.py
- prompt_templates.py
- provider_factory.py
- __init__.py
- schemas.py
- session.py
- - __init__.py
  - burp_api_plugin.py
  - feroxbuster_plugin.py
  - metasploit_plugin.py
- __init__.py
- plugin_base.py
- plugin_loader.py
- chai-network-security-test.png
- __init__.py
- analyze.py
- auth.py
- autonomous.py
- base.py
- exec.py
- injection.py
- network.py
- poc.py
- recon.py
- report.py
- scan.py
- __init__.py
- command_parser.py
- cvss_calculator.py
- output_parser.py
.gitignore
.security.yml.example
__init__.py
app_context.py
chai-banner.png
config.py
config.yaml.example
LICENSE
main.py
README.md
REQUIRED_PEN_TEST_DEPENDENCIES.md
requirements.txt

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Cyber Host Artificial Intelligence (C.H.A.I)

A production-ready, autonomous penetration testing MCP (Model Context Protocol) server with an integrated AI decision engine, multi-provider LLM support, and an extensible plugin architecture. Designed for Raspberry Pi 4/5 running Kali Linux ARM64.

<p align="center">
	
<img src="./chai-banner.png" width="90%" />
</p>
## Architecture Overview

```
External Client (CHAI / any MCP tool)
         │  MCP stdio/SSE
         ▼
┌─────────────────────────────────────────┐
│         MCP Security Server             │
│                                         │
│  run_autonomous_scan()                  │
│         │                               │
│    ┌────▼────────────────────┐          │
│    │   execution_loop.py     │          │
│    │  (local, no LLM here)   │          │
│    │  tool1 → tool2 → tool3  │          │
│    └────┬────────────────────┘          │
│         │ at phase boundaries only      │
│    ┌────▼────────────────────┐          │
│    │   ai_planner.py         │          │
│    │  plan / evaluate /      │◄─────────┼── llm/provider_factory.py
│    │  summarize              │          │   (Azure / OpenAI / Claude /
│    └─────────────────────────┘          │    Bedrock / OpenRouter / HF)
│                                         │
│  All tools, safety, sandbox unchanged   │
└─────────────────────────────────────────┘
```

**Design Philosophy: THIN BRAIN, THICK LOOP**

- The internal LLM fires only at **decision boundaries**, not per-step
- A local `execution_loop` handles tool chaining deterministically between LLM calls
- Keeps token usage low (~6-10 calls per full pentest) and latency acceptable on a Pi 4

## Features

### Multi-Provider LLM Support

- **Azure OpenAI** (GPT-4.1, GPT-4o, GPT-5+, Kimi, DeepSeek via Azure AI Foundry)
- **Direct OpenAI** (GPT-4.1, GPT-4o, etc.)
- **Anthropic Claude** (Sonnet, Opus)
- **Amazon Bedrock** (Claude, Titan, Llama via AWS)
- **OpenRouter** (100+ models with one key)
- **HuggingFace** (DeepSeek, Qwen, Llama via Inference API)

### AI Decision Engine

- **plan()**: Decides what to test next based on findings
- **evaluate()**: Decides whether to continue or stop
- **summarize_for_report()**: Generates executive summary and remediation priorities

### Security & Sandboxing

- **firejail** profiles with rlimit restrictions
- **Linux cgroups** for resource limiting
- **Restricted user** (`pentester`) execution
- **Tiered safety policy** (Tier 1/2/3)
- **Immutable audit logging** of all commands and AI decisions

### Plugin System

- Auto-discovers plugins from `plugins/bundled/` and `plugins/external/`
- Drop-in plugin architecture — no core changes needed
- Bundled plugins: Feroxbuster, Metasploit, Burp Suite API

### Database

- **SQLite ONLY** — no Neo4j, Redis, or Postgres required
- WAL mode for better concurrency
- Knowledge graph with 50+ attack techniques and recursive CTE chain queries

## Project Structure

```
CHAI/
├── main.py                          # FastMCP server entry point
├── config.py                        # Configuration loader
├── config.yaml                      # Main configuration (no secrets)
├── .security.yml                    # API keys (git-ignored)
├── requirements.txt                 # Python dependencies
├── app_context.py                   # Application context singleton
│
├── llm/                             # Multi-provider LLM adapter layer
│   ├── base_provider.py             # Abstract base class
│   ├── provider_factory.py          # Provider selection with fallback
│   ├── prompt_templates.py          # All LLM prompts (versioned)
│   └── providers/
│       ├── azure_openai.py          # Azure OpenAI
│       ├── openai_direct.py         # Direct OpenAI
│       ├── anthropic_claude.py      # Claude
│       ├── amazon_bedrock.py        # AWS Bedrock
│       ├── openrouter.py            # OpenRouter
│       └── huggingface.py           # HuggingFace
│
├── core/                            # Core engine
│   ├── session_manager.py           # SQLite session CRUD + state machine
│   ├── safety_policy.py             # Command validation, tier system
│   ├── process_controller.py        # firejail/cgroups/chroot wrapper
│   ├── audit_logger.py              # Immutable audit logging
│   ├── ai_planner.py                # LLM decision engine (3 call types)
│   └── execution_loop.py            # Local chain runner
│
├── kb/                              # Knowledge Base
│   ├── graph_db.py                  # Attack graph with recursive CTE
│   ├── playbook_loader.py           # Playbook section extraction
│   └── vector_search.py             # Vector/BM25 search
│
├── tools/                           # Security testing tools
│   ├── base.py                      # Base tool class
│   ├── recon.py                     # Reconnaissance
│   ├── scan.py                      # Vulnerability scanning
│   ├── injection.py                 # Injection testing
│   ├── auth.py                      # Authentication testing
│   ├── network.py                   # Network testing
│   ├── poc.py                       # PoC generation
│   ├── exec.py                      # Custom command execution
│   ├── analyze.py                   # Findings analysis
│   ├── report.py                    # Report generation
│   └── autonomous.py                # Autonomous scan orchestrator
│
├── plugins/                         # Plugin system
│   ├── plugin_base.py               # Base class
│   ├── plugin_loader.py             # Auto-discovery loader
│   └── bundled/
│       ├── feroxbuster_plugin.py    # Directory bruteforcer
│       ├── metasploit_plugin.py     # Metasploit Framework
│       └── burp_api_plugin.py      # Burp Suite Pro API
│
├── models/                          # Data models
│   ├── session.py                   # Session and Finding models
│   └── schemas.py                   # Pydantic schemas
│
├── utils/                           # Utilities
│   ├── command_parser.py            # Command parsing
│   ├── output_parser.py             # Tool output parsing
│   └── cvss_calculator.py           # CVSS v3.1 calculator
│
└── data/                            # Database schemas & profiles
    ├── init_sessions.sql            # Session DB schema + AI decisions table
    ├── init_graph.sql               # Knowledge graph (50+ nodes)
    └── firejail/
        └── pentest.profile          # Firejail sandbox profile
```

## Installation

### Prerequisites

- Any linux machine / Raspberry Pi 4/5 with Kali Linux ARM64 (bare metal, NO Docker)
- Python 3.11+
- firejail installed
- Kali Linux pentest tools (nmap, sqlmap, nuclei, ffuf, etc.)

### Setup

```bash
# Clone the repository
git clone https://github.com/NIHAR-SARKAR/CHAI.git
cd CHAI

# Create virtual environment
python -m venv .venv
source .venv/bin/activate -- linux
.venv\Scripts\activate    -- windows

# Install dependencies
pip install -r requirements.txt

# Configure secrets
cp .security.yml.example .security.yml
chmod 600 .security.yml
# Edit .security.yml with your API keys

# Create required directories
### linux
sudo mkdir -p /opt/sessions /opt/logs /opt/kb /opt/mcp-security-server/plugins/external
sudo chown -R $(whoami) /opt/sessions /opt/logs /opt/kb

### windows PowerSheel
New-Item -ItemType Directory -Force -Path "C:\opt\sessions"
New-Item -ItemType Directory -Force -Path "C:\opt\logs"
New-Item -ItemType Directory -Force -Path "C:\opt\kb"
New-Item -ItemType Directory -Force -Path "C:\opt\mcp-security-server\plugins\external"

icacls "C:\opt" /grant "$env:USERNAME:(OI)(CI)F" /Ts -- Grant current user full permissions

# Install firejail profile
sudo cp data/firejail/pentest.profile /etc/firejail/

# run server
python main.py --transport streamable-http
```

## Configuration

### config.yaml (Main Config)

Edit `config.yaml` to configure:

- Server transport (stdio or SSE)
- Sandbox limits (RAM, CPU, timeout)
- LLM provider selection
- Plugin enable/disable

Key sections:

```yaml
llm:
  active_provider: "azure_openai" # Change to your preferred provider
  fallback_provider: "openrouter" # Optional fallback

ai_planner:
  max_phases: 4 # Max autonomous phases
  stop_on_critical: true # Stop on critical findings

plugins:
  bundled:
    feroxbuster: true
    metasploit: false # Disabled by default (Tier 3)
    burp_api: false # Needs Burp Pro API key
```

### .security.yml (Secrets)

```yaml
# NEVER commit this file
azure_openai:
  api_key: "your-azure-key"

openai:
  api_key: "your-openai-key"

anthropic:
  api_key: "your-anthropic-key"

# ... etc for each provider
```

### CHAI Integration

Add to your CHAI `config.json`:

**stdio transport:**

```json
{
  "tools": {
    "mcp": {
      "servers": {
        "chai-security": {
          "transport": "stdio",
          "command": "python",
          "args": ["-m", "main.py"],
          "cwd": "/opt/mcp-security-server",
          "env": {
            "PYTHONPATH": "/opt/mcp-security-server"
          },
          "discovery": "deferred"
        }
      }
    }
  }
}
```

**SSE transport (for remote Pi access):**

```json
{
  "tools": {
    "mcp": {
      "servers": {
        "chai-security": {
          "transport": "sse",
          "url": "http://raspberrypi.local:9010/sse"
        }
      }
    }
  }
}
```

## Usage

### Initialize a Session

```python
initialize_session(
    target="https://target.example.com",
    test_type="web_app",
    scope=["target.example.com"]
)
# Returns: {"session_id": "sess-abc-123", ...}
```

### Run Autonomous Scan (One Call, Complete Test)

```python
run_autonomous_scan(
    session_id="sess-abc-123",
    max_phases=4,
    stop_on_critical=True,
    generate_report=True,
    provider_override=None  # Uses config.yaml active_provider
)
# Internally: plan → [recon → scan → inject] → evaluate → plan → [...] → report
# Returns after ~15-30 min:
# {
#   "phases_completed": 3,
#   "total_findings": 12,
#   "critical_count": 1,
#   "high_count": 4,
#   "report_path": "/opt/sessions/reports/sess-abc-123.md",
#   "status": "complete"
# }
```

### Manual Tool Calls

```python
# Reconnaissance
run_recon(session_id="sess-abc-123", target="target.example.com", recon_type="passive")

# Vulnerability scanning
scan_vulnerabilities(session_id="sess-abc-123", target="target.example.com", scanner="nuclei")

# Injection testing
test_injection(session_id="sess-abc-123", target="target.example.com", injection_type="sqli")

# Authentication testing
test_authentication(session_id="sess-abc-123", target="target.example.com", test_type="bypass")

# Network testing
test_network(session_id="sess-abc-123", target="target.example.com", test_type="ssl")

# Custom command
execute_command(session_id="sess-abc-123", command="nmap -sV target.example.com")

# Run plugin
run_plugin(session_id="sess-abc-123", plugin_name="feroxbuster", target="https://target.example.com")

# Generate report
generate_report(session_id="sess-abc-123", format="markdown")

# Check status
get_session_status(session_id="sess-abc-123")

# Emergency stop
emergency_stop(session_id="sess-abc-123")
```

## Adding a New LLM Provider

**Step 1** — Create `llm/providers/gemini.py`:

```python
from llm.base_provider import BaseLLMProvider, LLMResponse

class GeminiProvider(BaseLLMProvider):
    def __init__(self, config): ...
    @property
    def provider_name(self): return "gemini"
    async def complete(self, ...): ...
    async def health_check(self): ...
```

**Step 2** — Add one `case` to `llm/provider_factory.py`:

```python
case "gemini":
    from llm.providers.gemini import GeminiProvider
    return GeminiProvider(config)
```

**Step 3** — Add config block to `config.yaml`:

```yaml
llm:
  gemini:
    enabled: true
    model: "gemini-2.5-pro"
    api_base: "https://generativelanguage.googleapis.com/v1beta/openai"
```

**Step 4** — Add key to `.security.yml`:

```yaml
gemini:
  api_key: ""
```

**Step 5** — Change `active_provider: "gemini"` in `config.yaml`.

**That's it. No other files change.**

## Adding a New Pentest Plugin

**Step 1** — Create `plugins/external/gospider_plugin.py`:

```python
from plugins.plugin_base import PentestPlugin, PluginMetadata, PluginResult

class GospiderPlugin(PentestPlugin):
    @property
    def metadata(self):
        return PluginMetadata(
            name="gospider", display_name="GoSpider Web Crawler",
            version="1.1.6", description="Fast web spider",
            tier="tier1", requires_binary="gospider",
            tags=["web", "recon", "crawler"],
        )
    async def run(self, session_id, target, args, process_controller, safety_policy, session_manager):
        # Build command, validate through safety_policy, execute via process_controller
        ...
```

**Step 2** — Restart the server. The plugin auto-loads.

**That's it. No changes to core application.**

## LLM Call Budget

For a 4-phase autonomous scan:

- Phase 1: plan() + evaluate() = 2 calls
- Phase 2: plan() + evaluate() = 2 calls
- Phase 3: plan() + evaluate() = 2 calls
- Phase 4: plan() + evaluate() = 2 calls
- Report: summarize_for_report() = 1 call
- **Total: ~9 LLM calls per full pentest**

This keeps token usage low and latency acceptable on a Raspberry Pi 4.

## Safety & Compliance

- **Command denylist**: Dangerous commands (rm -rf /, fork bombs, etc.) are blocked
- **Tier system**: Tools classified by risk (Tier 1/2/3)
- **Scope checking**: Commands validated against defined scope
- **Rate limiting**: Per-tier concurrent execution limits
- **Sandboxing**: All commands run through firejail with resource limits
- **Audit trail**: Every command and AI decision is logged immutably

## License

MIT License — See LICENSE file for details.
![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)

## Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests (pytest)
5. Submit a pull request

## Support

For issues and questions:

- GitHub Issues: [https://github.com/NIHAR-SARKAR/CHAI/issues](https://github.com/NIHAR-SARKAR/CHAI/issues)
- Documentation: [https://github.com/NIHAR-SARKAR/CHAI/blob/main/README.md](https://github.com/NIHAR-SARKAR/CHAI/blob/main/README.md)
- Site Url: [https://aithread.in](https://aithread.in)

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