BlackSnufkin
636 lines
28 KiB
Python
636 lines
28 KiB
Python
import logging
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import json
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from typing import Optional, Dict, List, Any
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from mcp.server.fastmcp import FastMCP
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# Import the optimized LitterBox client
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from optimized_litterbox_client import LitterBoxClient, LitterBoxError, LitterBoxAPIError
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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# Initialize MCP server with enhanced focus on OPSEC analysis
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mcp = FastMCP(
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name="LitterBoxOpsecMCP",
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instructions=(
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"Elite payload OPSEC analysis framework specializing in detection evasion.\n"
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"Analyze YARA signatures, behavioral patterns, EDR triggers, and attribution risks.\n"
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"Provide actionable tradecraft improvements for bypassing modern security controls.\n"
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"Focus on signature evasion, behavioral stealth, attribution avoidance, and deployment readiness.\n"
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"Emphasize practical OPSEC improvements with specific remediation strategies.\n"
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"Support both payload analysis and kernel driver BYOVD assessment."
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),
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)
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class LitterBoxMCPClient:
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"""Enhanced MCP client wrapper with connection management"""
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def __init__(self, base_url: str = "http://127.0.0.1:1337", timeout: int = 120):
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self.base_url = base_url
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self.timeout = timeout
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self._client = None
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def get_client(self) -> LitterBoxClient:
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"""Get or create client instance with connection pooling"""
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if self._client is None:
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self._client = LitterBoxClient(
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base_url=self.base_url,
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timeout=self.timeout,
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logger=logger
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)
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return self._client
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def close(self):
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"""Close client connection"""
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if self._client:
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self._client.close()
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self._client = None
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# Initialize enhanced client wrapper
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mcp_client = LitterBoxMCPClient()
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def handle_api_operation(operation_name: str, callable_fn, *args, **kwargs) -> Dict[str, Any]:
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"""Enhanced error handling with operation context and detailed logging"""
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try:
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logger.debug(f"Executing operation: {operation_name}")
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result = callable_fn(*args, **kwargs)
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logger.debug(f"Operation {operation_name} completed successfully")
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return {
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"status": "success",
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"operation": operation_name,
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"data": result,
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"timestamp": None # Could add timestamp if needed
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}
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except LitterBoxAPIError as e:
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error_msg = f"API error in {operation_name}: {str(e)}"
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logger.error(error_msg)
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return {
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"status": "api_error",
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"operation": operation_name,
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"message": str(e),
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"http_code": e.status_code,
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"response_data": e.response
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}
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except LitterBoxError as e:
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error_msg = f"Client error in {operation_name}: {str(e)}"
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logger.error(error_msg)
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return {
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"status": "client_error",
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"operation": operation_name,
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"message": str(e)
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}
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except Exception as e:
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error_msg = f"Unexpected error in {operation_name}: {str(e)}"
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logger.error(error_msg, exc_info=True)
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return {
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"status": "error",
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"operation": operation_name,
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"message": str(e),
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"type": type(e).__name__
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}
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# =============================================================================
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# CORE PAYLOAD ANALYSIS TOOLS
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# =============================================================================
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@mcp.tool(name="upload_payload", description="Upload payload for comprehensive OPSEC analysis")
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def upload_payload(path: str, name: Optional[str] = None) -> Dict[str, Any]:
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"""Upload payload and prepare for analysis"""
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client = mcp_client.get_client()
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return handle_api_operation("upload_payload", client.upload_file, path, file_name=name)
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@mcp.tool(name="upload_kernel_driver", description="Upload kernel driver for BYOVD analysis")
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def upload_kernel_driver(path: str, name: Optional[str] = None, run_holygrail: bool = True) -> Dict[str, Any]:
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"""Upload kernel driver and optionally run HolyGrail BYOVD analysis"""
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client = mcp_client.get_client()
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return handle_api_operation("upload_kernel_driver",
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client.upload_and_analyze_driver, path,
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file_name=name, run_holygrail=run_holygrail)
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@mcp.tool(name="analyze_static", description="Run comprehensive static analysis - YARA signatures, PE structure, imports")
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def analyze_static(file_hash: str, wait_completion: bool = True) -> Dict[str, Any]:
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"""Execute static analysis to identify signature detections and file characteristics"""
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client = mcp_client.get_client()
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return handle_api_operation("analyze_static",
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client.analyze_file, file_hash, 'static',
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wait_for_completion=wait_completion)
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@mcp.tool(name="analyze_dynamic", description="Run dynamic analysis - behavioral detection, runtime artifacts, process analysis")
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def analyze_dynamic(target: str, cmd_args: Optional[List[str]] = None, wait_completion: bool = True) -> Dict[str, Any]:
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"""Execute dynamic analysis to test behavioral evasion and runtime stealth"""
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client = mcp_client.get_client()
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return handle_api_operation("analyze_dynamic",
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client.analyze_file, target, 'dynamic',
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cmd_args=cmd_args, wait_for_completion=wait_completion)
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@mcp.tool(name="analyze_holygrail", description="Run HolyGrail BYOVD analysis on kernel drivers")
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def analyze_holygrail(file_hash: str, wait_completion: bool = True) -> Dict[str, Any]:
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"""Execute HolyGrail analysis for kernel driver vulnerability assessment"""
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client = mcp_client.get_client()
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return handle_api_operation("analyze_holygrail",
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client.analyze_holygrail, file_hash,
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wait_for_completion=wait_completion)
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# =============================================================================
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# RESULT RETRIEVAL TOOLS
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# =============================================================================
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@mcp.tool(name="get_comprehensive_results", description="Get all available analysis results for target")
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def get_comprehensive_results(target: str) -> Dict[str, Any]:
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"""Retrieve all analysis results (static, dynamic, file info, HolyGrail)"""
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client = mcp_client.get_client()
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return handle_api_operation("get_comprehensive_results",
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client.get_comprehensive_results, target)
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@mcp.tool(name="get_file_info", description="Get file metadata, entropy analysis, and PE structure")
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def get_file_info(file_hash: str) -> Dict[str, Any]:
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"""Retrieve detailed file information and characteristics"""
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client = mcp_client.get_client()
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return handle_api_operation("get_file_info", client.get_file_info, file_hash)
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@mcp.tool(name="get_static_results", description="Get static analysis results - YARA matches, signatures, imports")
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def get_static_results(file_hash: str) -> Dict[str, Any]:
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"""Retrieve comprehensive static analysis results"""
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client = mcp_client.get_client()
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return handle_api_operation("get_static_results", client.get_static_results, file_hash)
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@mcp.tool(name="get_dynamic_results", description="Get dynamic analysis results - behavioral detections, runtime artifacts")
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def get_dynamic_results(target: str) -> Dict[str, Any]:
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"""Retrieve comprehensive dynamic analysis results"""
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client = mcp_client.get_client()
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return handle_api_operation("get_dynamic_results", client.get_dynamic_results, target)
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@mcp.tool(name="get_holygrail_results", description="Get HolyGrail BYOVD analysis results")
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def get_holygrail_results(target: str) -> Dict[str, Any]:
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"""Retrieve HolyGrail/BYOVD analysis results for kernel drivers"""
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client = mcp_client.get_client()
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return handle_api_operation("get_holygrail_results", client.get_holygrail_results, target)
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# =============================================================================
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# DOPPELGANGER COMPARISON TOOLS
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# =============================================================================
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@mcp.tool(name="run_blender_scan", description="Run system-wide Blender scan for baseline comparison")
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def run_blender_scan() -> Dict[str, Any]:
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"""Execute system-wide Blender scan to establish clean baseline"""
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client = mcp_client.get_client()
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return handle_api_operation("run_blender_scan", client.run_blender_scan)
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@mcp.tool(name="compare_with_blender", description="Compare payload against system baseline with Blender")
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def compare_with_blender(file_hash: str) -> Dict[str, Any]:
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"""Compare payload execution against clean system baseline"""
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client = mcp_client.get_client()
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return handle_api_operation("compare_with_blender", client.compare_with_blender, file_hash)
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@mcp.tool(name="create_fuzzy_database", description="Create fuzzy hash database for similarity analysis")
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def create_fuzzy_database(folder_path: str, extensions: Optional[List[str]] = None) -> Dict[str, Any]:
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"""Build fuzzy hash database from known payload collection"""
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client = mcp_client.get_client()
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return handle_api_operation("create_fuzzy_database",
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client.create_fuzzy_database, folder_path, extensions)
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@mcp.tool(name="analyze_fuzzy_similarity", description="Analyze payload similarity using fuzzy hashing")
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def analyze_fuzzy_similarity(file_hash: str, threshold: int = 85) -> Dict[str, Any]:
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"""Compare payload against fuzzy hash database for attribution analysis"""
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client = mcp_client.get_client()
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return handle_api_operation("analyze_fuzzy_similarity",
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client.analyze_with_fuzzy, file_hash, threshold)
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# =============================================================================
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# PROCESS ANALYSIS TOOLS
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# =============================================================================
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@mcp.tool(name="validate_pid", description="Validate process ID before dynamic analysis")
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def validate_pid(pid: int) -> Dict[str, Any]:
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"""Verify process exists and is accessible for analysis"""
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client = mcp_client.get_client()
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return handle_api_operation("validate_pid", client.validate_process, pid)
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@mcp.tool(name="analyze_running_process", description="Analyze currently running process for OPSEC assessment")
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def analyze_running_process(pid: int, cmd_args: Optional[List[str]] = None) -> Dict[str, Any]:
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"""Analyze live process for behavioral patterns and detection indicators"""
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client = mcp_client.get_client()
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return handle_api_operation("analyze_running_process",
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client.analyze_file, str(pid), 'dynamic', cmd_args=cmd_args)
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# =============================================================================
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# REPORT AND DOCUMENTATION TOOLS
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# =============================================================================
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@mcp.tool(name="generate_opsec_report", description="Generate comprehensive OPSEC analysis report")
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def generate_opsec_report(target: str, download: bool = False) -> Dict[str, Any]:
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"""Generate detailed OPSEC analysis report with findings and recommendations"""
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client = mcp_client.get_client()
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return handle_api_operation("generate_opsec_report",
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client.get_report, target, download=download)
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@mcp.tool(name="download_analysis_report", description="Download analysis report to specified location")
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def download_analysis_report(target: str, output_path: Optional[str] = None) -> Dict[str, Any]:
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"""Download comprehensive analysis report for offline review"""
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client = mcp_client.get_client()
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return handle_api_operation("download_analysis_report",
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client.download_report, target, output_path)
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# =============================================================================
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# SYSTEM MANAGEMENT TOOLS
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# =============================================================================
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@mcp.tool(name="list_analyzed_payloads", description="List all analyzed payloads with OPSEC assessment summary")
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def list_analyzed_payloads() -> Dict[str, Any]:
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"""Retrieve summary of all tested payloads with risk assessments"""
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client = mcp_client.get_client()
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return handle_api_operation("list_analyzed_payloads", client.get_files_summary)
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@mcp.tool(name="get_system_status", description="Get comprehensive system health and analysis status")
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def get_system_status() -> Dict[str, Any]:
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"""Check sandbox health, tool status, and analysis queue"""
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client = mcp_client.get_client()
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return handle_api_operation("get_system_status", client.get_system_status)
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@mcp.tool(name="cleanup_analysis_artifacts", description="Clean up all testing artifacts and temporary files")
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def cleanup_analysis_artifacts(include_uploads: bool = True,
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include_results: bool = True,
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include_analysis: bool = True) -> Dict[str, Any]:
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"""Remove all testing artifacts from sandbox environment"""
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client = mcp_client.get_client()
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return handle_api_operation("cleanup_analysis_artifacts",
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client.cleanup, include_uploads, include_results, include_analysis)
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@mcp.tool(name="delete_payload", description="Delete specific payload and all associated analysis results")
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def delete_payload(file_hash: str) -> Dict[str, Any]:
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"""Permanently remove payload and all analysis artifacts"""
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client = mcp_client.get_client()
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return handle_api_operation("delete_payload", client.delete_file, file_hash)
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@mcp.tool(name="check_sandbox_health", description="Verify sandbox tools and analysis engines are operational")
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def check_sandbox_health() -> Dict[str, Any]:
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"""Comprehensive health check of all analysis components"""
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client = mcp_client.get_client()
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return handle_api_operation("check_sandbox_health", client.check_health)
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# =============================================================================
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# ENHANCED OPSEC-FOCUSED PROMPTS
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# =============================================================================
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@mcp.prompt()
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def analyze_detection_patterns(file_hash: str = "") -> str:
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"""Comprehensive detection pattern analysis with specific evasion recommendations"""
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return f"""Perform comprehensive detection pattern analysis for {f'payload {file_hash}' if file_hash else 'the target payload'}:
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## YARA Signature Analysis
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- Rule matches and triggered detection logic
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- String patterns causing signature hits
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- Behavioral rules triggered during execution
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- Custom rule development for testing
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## Static Analysis Detection Points
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- PE structure anomalies flagged
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- Import table suspicious patterns
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- File entropy and packing indicators
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- Metadata and compilation artifacts
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## Dynamic Behavioral Detection
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- Process manipulation techniques detected
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- Memory artifacts flagged by Moneta/PE-Sieve
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- API call patterns triggering alerts
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- Network communication behaviors
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- File system activity patterns
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## EDR/AV Evasion Strategy
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- Signature modification requirements
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- Behavioral pattern adjustments needed
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- Obfuscation and packing recommendations
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- Alternative implementation approaches
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- Anti-analysis technique improvements
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## Attribution Risk Assessment
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- Similarity to known offensive tools
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- Framework-specific behavioral patterns
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- Unique implementation fingerprints
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- Metadata revealing tool origin
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Provide specific, actionable remediation steps for each detection vector."""
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@mcp.prompt()
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def assess_evasion_effectiveness(file_hash: str = "") -> str:
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"""Detailed evasion effectiveness assessment with improvement roadmap"""
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return f"""Evaluate comprehensive evasion effectiveness for {f'payload {file_hash}' if file_hash else 'the target payload'}:
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## Signature Evasion Assessment
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- **YARA Rule Bypass**: Success rate and failed patterns
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- **AV Signature Avoidance**: Engine-specific detection rates
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- **String Obfuscation**: Effectiveness of current techniques
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- **Import Masking**: API hiding and resolution evasion
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## Behavioral Evasion Assessment
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- **EDR Behavioral Rules**: Triggered behaviors and bypass status
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- **Process Manipulation**: Stealth level of injection techniques
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- **Memory Artifacts**: Footprint visibility and cleanup effectiveness
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- **API Hooking Detection**: Bypass success for monitoring evasion
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## Runtime Stealth Evaluation
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- **Execution Flow**: Natural vs suspicious behavior patterns
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- **Resource Usage**: CPU, memory, and I/O pattern analysis
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- **Timing Analysis**: Execution speed and delay patterns
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- **Communication Stealth**: Network behavior obfuscation
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## Improvement Priority Matrix
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1. **CRITICAL**: Immediate signature bypass requirements
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2. **HIGH**: Behavioral detection evasion improvements
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3. **MEDIUM**: Attribution masking and fingerprint reduction
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4. **LOW**: General stealth and efficiency optimizations
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## Specific Enhancement Recommendations
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- Code modification requirements for signature evasion
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- Behavioral adjustment strategies for EDR bypass
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- Obfuscation technique upgrades needed
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- Alternative implementation methodologies
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Provide concrete implementation steps with expected effectiveness improvements."""
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@mcp.prompt()
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def analyze_attribution_risks(file_hash: str = "") -> str:
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"""In-depth attribution risk analysis with masking strategies"""
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return f"""Conduct thorough attribution risk analysis for {f'payload {file_hash}' if file_hash else 'the target payload'}:
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## Framework Attribution Indicators
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- **Metasploit/Cobalt Strike**: Known behavioral patterns and signatures
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- **Custom Framework**: Unique implementation characteristics
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- **Commercial Tools**: Licensed software patterns and artifacts
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- **Open Source**: GitHub/public repository code similarities
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## Code Attribution Vectors
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- **Compilation Artifacts**: Compiler version, build environment indicators
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- **Code Style**: Programming patterns and implementation choices
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- **Library Usage**: Specific dependency patterns and versions
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- **Error Handling**: Unique error message patterns and responses
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## Behavioral Attribution Patterns
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- **Technique Selection**: Preferred attack methods and sequences
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- **Configuration**: Default settings and parameter choices
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- **Communication**: Protocol preferences and data formats
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- **Persistence**: Installation and survival method preferences
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## Infrastructure Attribution Risks
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- **C2 Communication**: Protocol patterns and encryption methods
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- **Domain Generation**: Algorithm patterns and naming conventions
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- **Certificate Usage**: SSL/TLS certificate patterns and authorities
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- **Network Behavior**: Traffic patterns and timing characteristics
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## Fuzzy Hash Similarity Analysis
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- Similarity scores against known tool databases
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- Clustering analysis with existing payload families
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- Evolutionary relationship mapping to known tools
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- Variation analysis from base tool implementations
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## Attribution Masking Strategy
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- **Code Diversification**: Implementation variation recommendations
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- **Behavioral Modification**: Pattern disruption techniques
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- **Metadata Sanitization**: Compilation and build artifact removal
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- **Similarity Reduction**: Fuzzy hash distance optimization
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## Risk Mitigation Priorities
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1. Eliminate high-confidence attribution vectors
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2. Reduce fuzzy hash similarity scores below threshold
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3. Diversify behavioral implementation patterns
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4. Sanitize compilation and development artifacts
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Provide specific masking techniques with attribution risk reduction estimates."""
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@mcp.prompt()
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def generate_opsec_improvement_plan(file_hash: str = "") -> str:
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"""Create comprehensive, prioritized OPSEC improvement plan"""
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return f"""Generate detailed OPSEC improvement plan for {f'payload {file_hash}' if file_hash else 'the target payload'}:
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## Current OPSEC Assessment
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### Detection Status
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- **Static Detections**: YARA hits, signature matches, file characteristics
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- **Dynamic Detections**: Behavioral alerts, runtime artifacts, process analysis
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- **Attribution Risks**: Tool similarity, framework patterns, code fingerprints
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### Risk Categorization
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```
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Risk Level | Detection Type | Count | Severity | Impact
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-----------|----------------|-------|----------|--------
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CRITICAL | Signatures | X | HIGH | Deployment Blocker
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HIGH | Behavioral | X | MED | EDR Detection Risk
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MEDIUM | Attribution | X | LOW | Investigation Risk
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LOW | Metadata | X | MIN | Forensic Analysis
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```
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## Improvement Implementation Plan
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### Phase 1: Critical Detection Elimination (Week 1)
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- **Signature Evasion**:
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* Modify detected strings and patterns
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* Implement advanced obfuscation
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* Test against signature databases
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- **Immediate Blockers**:
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* Fix compilation artifacts
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* Remove obvious tool indicators
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* Sanitize metadata fields
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### Phase 2: Behavioral Stealth Enhancement (Week 2-3)
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- **Process Behavior**:
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* Implement natural execution flows
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* Add timing variations and delays
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* Minimize suspicious API usage patterns
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- **Memory Management**:
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* Improve artifact cleanup
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* Reduce detectable footprints
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* Enhance anti-analysis resistance
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### Phase 3: Attribution Risk Mitigation (Week 4)
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- **Code Diversification**:
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* Implement alternative techniques
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* Vary implementation patterns
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* Reduce fuzzy hash similarities
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- **Fingerprint Elimination**:
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* Remove framework-specific patterns
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* Customize error handling and responses
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* Modify communication protocols
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### Phase 4: Validation and Testing (Week 5)
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- **Comprehensive Re-testing**:
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* Full static and dynamic analysis
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* Multi-engine signature testing
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|
* Behavioral pattern verification
|
|
- **Deployment Readiness**:
|
|
* Final OPSEC assessment
|
|
* Risk acceptance evaluation
|
|
* Monitoring requirement definition
|
|
|
|
## Success Metrics and KPIs
|
|
- **Signature Detection Rate**: Target <5%
|
|
- **Behavioral Alert Count**: Target 0 critical alerts
|
|
- **Attribution Confidence**: Target <30% similarity
|
|
- **Overall Stealth Score**: Target >85%
|
|
|
|
## Resource Requirements
|
|
- Development time: X person-hours
|
|
- Testing infrastructure: Multi-engine analysis setup
|
|
- Validation tools: Updated signature databases
|
|
- Quality assurance: Independent OPSEC review
|
|
|
|
## Risk Assessment Post-Implementation
|
|
- Residual detection probability
|
|
- Attribution risk evaluation
|
|
- Operational security impact
|
|
- Incident response considerations
|
|
|
|
Provide specific, time-bound implementation steps with measurable success criteria."""
|
|
|
|
@mcp.prompt()
|
|
def evaluate_deployment_readiness(file_hash: str = "") -> str:
|
|
"""Comprehensive deployment readiness assessment with go/no-go decision framework"""
|
|
return f"""Evaluate operational deployment readiness for {f'payload {file_hash}' if file_hash else 'the target payload'}:
|
|
|
|
## Deployment Readiness Criteria
|
|
|
|
### Technical Assessment Matrix
|
|
```
|
|
Category | Status | Score | Weight | Weighted Score | Blocker
|
|
---------------------|--------|-------|--------|----------------|--------
|
|
Signature Evasion | P/F | 0-100 | 30% | XX/30 | Y/N
|
|
Behavioral Stealth | P/F | 0-100 | 25% | XX/25 | Y/N
|
|
Attribution Masking | P/F | 0-100 | 20% | XX/20 | Y/N
|
|
Technical Function | P/F | 0-100 | 15% | XX/15 | Y/N
|
|
Anti-Analysis | P/F | 0-100 | 10% | XX/10 | Y/N
|
|
---------------------|--------|-------|--------|----------------|--------
|
|
TOTAL READINESS | | | 100% | XX/100 |
|
|
```
|
|
|
|
### Detailed Assessment Criteria
|
|
|
|
#### Signature Evasion (30% Weight)
|
|
- **YARA Rules**: Zero matches required for deployment
|
|
- **AV Engines**: <5% detection rate across major vendors
|
|
- **Static Analysis**: Clean file characteristics and entropy
|
|
- **String Analysis**: No obvious payload indicators
|
|
|
|
#### Behavioral Stealth (25% Weight)
|
|
- **EDR Bypass**: No behavioral rule triggers
|
|
- **Process Analysis**: Natural execution patterns
|
|
- **Memory Artifacts**: Minimal detectable footprint
|
|
- **API Usage**: Legitimate application behavior
|
|
|
|
#### Attribution Masking (20% Weight)
|
|
- **Tool Similarity**: <30% fuzzy hash similarity to known tools
|
|
- **Framework Patterns**: No obvious Metasploit/CS/custom indicators
|
|
- **Code Fingerprints**: Unique implementation characteristics
|
|
- **Compilation Artifacts**: Sanitized build environment traces
|
|
|
|
#### Technical Functionality (15% Weight)
|
|
- **Core Features**: All primary functions operational
|
|
- **Error Handling**: Graceful failure and recovery
|
|
- **Compatibility**: Target environment compatibility verified
|
|
- **Performance**: Acceptable resource usage and speed
|
|
|
|
#### Anti-Analysis Resistance (10% Weight)
|
|
- **Sandbox Evasion**: Bypass common analysis environments
|
|
- **Debugging Protection**: Anti-debug and anti-VM features
|
|
- **Reverse Engineering**: Code obfuscation and protection
|
|
- **Dynamic Analysis**: Runtime analysis resistance
|
|
|
|
## Risk Assessment Framework
|
|
|
|
### Detection Probability Matrix
|
|
```
|
|
Environment Type | Detection Risk | Confidence | Mitigation
|
|
--------------------|----------------|------------|------------
|
|
Corporate SOC | HIGH/MED/LOW | XX% | [Strategy]
|
|
Advanced EDR | HIGH/MED/LOW | XX% | [Strategy]
|
|
Incident Response | HIGH/MED/LOW | XX% | [Strategy]
|
|
Forensic Analysis | HIGH/MED/LOW | XX% | [Strategy]
|
|
```
|
|
|
|
### Operational Risk Factors
|
|
- **Mission Impact**: Payload detection consequences
|
|
- **Attribution Risk**: Operator/infrastructure exposure probability
|
|
- **Incident Escalation**: Response team engagement likelihood
|
|
- **Recovery Complexity**: Remediation and re-engagement difficulty
|
|
|
|
## Deployment Decision Framework
|
|
|
|
### GO Decision Criteria (All Must Be Met)
|
|
- ✓ Total readiness score ≥85%
|
|
- ✓ Zero deployment blockers identified
|
|
- ✓ All critical and high-priority issues resolved
|
|
- ✓ Acceptable risk level for mission requirements
|
|
- ✓ Operational security review completed and approved
|
|
|
|
### NO-GO Decision Criteria (Any Present)
|
|
- ✗ Signature detection above threshold
|
|
- ✗ Critical behavioral detections present
|
|
- ✗ High-confidence attribution vectors identified
|
|
- ✗ Core functionality failures
|
|
- ✗ Unacceptable operational risk level
|
|
|
|
### CONDITIONAL GO Criteria
|
|
- Medium-priority issues with acceptable risk levels
|
|
- Non-critical attribution indicators below threshold
|
|
- Minor functionality limitations with workarounds
|
|
- Manageable operational risks with enhanced monitoring
|
|
|
|
## Final Recommendation
|
|
|
|
### Decision: **[GO / NO-GO / CONDITIONAL GO]**
|
|
|
|
### Supporting Rationale:
|
|
- Technical assessment summary
|
|
- Risk evaluation conclusion
|
|
- Mission requirement alignment
|
|
- Operational security considerations
|
|
|
|
### Required Actions Before Deployment:
|
|
1. [Specific technical fixes required]
|
|
2. [Additional testing requirements]
|
|
3. [Operational security measures]
|
|
4. [Monitoring and response preparations]
|
|
|
|
### Post-Deployment Requirements:
|
|
- Enhanced monitoring for XX period
|
|
- Incident response team notification
|
|
- Attribution monitoring and assessment
|
|
- Performance and effectiveness tracking
|
|
|
|
### Risk Acceptance Statement:
|
|
- Residual risks acknowledged and accepted
|
|
- Mitigation strategies implemented where possible
|
|
- Operational security measures in place
|
|
- Mission leadership approval obtained
|
|
|
|
Provide clear, actionable deployment recommendation with detailed supporting analysis."""
|
|
|
|
# =============================================================================
|
|
# SERVER INITIALIZATION AND CLEANUP
|
|
# =============================================================================
|
|
|
|
@mcp.tool(name="shutdown_client", description="Properly close MCP client connections")
|
|
def shutdown_client() -> Dict[str, Any]:
|
|
"""Clean shutdown of MCP client and connections"""
|
|
try:
|
|
mcp_client.close()
|
|
return {"status": "success", "message": "Client connections closed successfully"}
|
|
except Exception as e:
|
|
return {"status": "error", "message": f"Error during shutdown: {str(e)}"}
|
|
|
|
def cleanup_on_exit():
|
|
"""Cleanup function called on server shutdown"""
|
|
logger.info("Shutting down LitterBox MCP server...")
|
|
mcp_client.close()
|
|
logger.info("Cleanup completed")
|
|
|
|
if __name__ == "__main__":
|
|
try:
|
|
logger.info("Starting LitterBox OPSEC MCP Server...")
|
|
mcp.serve(host="0.0.0.0", port=50051)
|
|
except KeyboardInterrupt:
|
|
logger.info("Server shutdown requested")
|
|
finally:
|
|
cleanup_on_exit() |