mirror of
https://github.com/GH05TCREW/pentestagent.git
synced 2026-03-08 06:44:11 +00:00
770 lines
40 KiB
Python
770 lines
40 KiB
Python
import json
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import os
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import re
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import asyncio
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import threading
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import traceback
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from colorama import init, Fore, Back, Style
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from ollama import chat,Message
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import tiktoken
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# Import workflows
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try:
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from workflows import get_available_workflows, get_workflow_by_key, list_workflow_names
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WORKFLOWS_AVAILABLE = True
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except ImportError:
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WORKFLOWS_AVAILABLE = False
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init(autoreset=True)
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ASCII_TITLE = f"""
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{Fore.WHITE} ('-. .-. .-') .-') _ _ .-') ('-. (`\ .-') /`{Style.RESET_ALL}
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{Fore.WHITE} ( OO ) / ( OO ). ( OO) ) ( \( -O ) _( OO) `.( OO ),'{Style.RESET_ALL}
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{Fore.WHITE} ,----. ,--. ,--. .-'),-----. (_)---\_)/ '._ .-----. ,------. (,------.,--./ .--. {Style.RESET_ALL}
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{Fore.WHITE} ' .-./-') | | | |( OO' .-. '/ _ | |'--...__)' .--./ | /`. ' | .---'| | | {Style.RESET_ALL}
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{Fore.WHITE} | |_( O- )| .| |/ | | | |\ :` `. '--. .--'| |('-. | / | | | | | | | |, {Style.RESET_ALL}
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{Fore.WHITE} | | .--, \| |\_) | |\| | '..`''.) | | /_) |OO )| |_.' |(| '--. | |.'.| |_){Style.RESET_ALL}
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{Fore.WHITE}(| | '. (_/| .-. | \ | | | |.-._) \ | | || |`-'| | . '.' | .--' | | {Style.RESET_ALL}
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{Fore.WHITE} | '--' | | | | | `' '-' '\ / | | (_' '--'\ | |\ \ | `---.| ,'. | {Style.RESET_ALL}
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{Fore.WHITE} `------' `--' `--' `-----' `-----' `--' `-----' `--' '--' `------''--' '--' {Style.RESET_ALL}
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{Fore.WHITE}====================== GHOSTCREW ======================{Style.RESET_ALL}
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"""
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# Import Agent-related modules
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from agents import (
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Agent,
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Model,
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ModelProvider,
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OpenAIChatCompletionsModel,
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RunConfig,
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Runner,
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set_tracing_disabled,
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ModelSettings
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)
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from openai import AsyncOpenAI # OpenAI async client
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from openai.types.responses import ResponseTextDeltaEvent, ResponseContentPartDoneEvent
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from agents.mcp import MCPServerStdio # MCP server related
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from dotenv import load_dotenv # Environment variable loading
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from agents.mcp import MCPServerSse
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from rag_split import Kb # Import Kb class
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# Load .env file
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load_dotenv()
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# Set API-related environment variables
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API_KEY = os.getenv("OPENAI_API_KEY")
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BASE_URL = os.getenv("OPENAI_BASE_URL")
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MODEL_NAME = os.getenv("MODEL_NAME")
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# Check if environment variables are set
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if not API_KEY:
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raise ValueError("API key not set")
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if not BASE_URL:
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raise ValueError("API base URL not set")
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if not MODEL_NAME:
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raise ValueError("Model name not set")
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client = AsyncOpenAI(
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base_url=BASE_URL,
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api_key=API_KEY
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)
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# Disable tracing to avoid requiring OpenAI API key
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set_tracing_disabled(True)
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# Generic model provider class
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class DefaultModelProvider(ModelProvider):
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"""
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Model provider using OpenAI compatible interface
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"""
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def get_model(self, model_name: str) -> Model:
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return OpenAIChatCompletionsModel(model=model_name or MODEL_NAME, openai_client=client)
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# Create model provider instance
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model_provider = DefaultModelProvider()
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def get_available_tools(connected_servers):
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"""Get list of available/connected tool names"""
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return [server.name for server in connected_servers]
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async def run_automated_workflow(workflow, target, connected_servers, conversation_history, kb_instance):
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"""Execute an automated penetration testing workflow"""
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available_tools = get_available_tools(connected_servers)
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print(f"\n{Fore.CYAN}Starting Automated Workflow: {workflow['name']}{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}Target: {target}{Style.RESET_ALL}")
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print(f"{Fore.CYAN}Available Tools: {', '.join(available_tools) if available_tools else 'None'}{Style.RESET_ALL}")
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print(f"{Fore.WHITE}Description: {workflow['description']}{Style.RESET_ALL}")
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print(f"{Fore.WHITE}{'='*60}{Style.RESET_ALL}")
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results = []
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for i, step in enumerate(workflow['steps'], 1):
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print(f"\n{Fore.CYAN}Step {i}/{len(workflow['steps'])}{Style.RESET_ALL}")
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formatted_step = step.format(target=target)
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print(f"{Fore.WHITE}{formatted_step}{Style.RESET_ALL}")
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# Create comprehensive query for this step
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enhanced_query = f"""
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Execute the following penetration testing step as part of an automated workflow:
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TARGET: {target}
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STEP: {formatted_step}
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AVAILABLE_TOOLS: {', '.join(available_tools) if available_tools else 'No tools configured'}
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Please execute this step systematically using appropriate tools and provide:
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1. Detailed command outputs and technical findings
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2. Security vulnerabilities or issues discovered
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3. Risk assessment and severity analysis
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4. Specific recommendations for remediation
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5. Relevant technical details and evidence
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Be thorough and professional in your analysis. Use the available tools effectively.
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"""
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# Execute the step through the agent
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result = await run_agent(enhanced_query, connected_servers, history=conversation_history, streaming=True, kb_instance=kb_instance)
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if result and hasattr(result, "final_output"):
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results.append({
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"step": i,
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"description": formatted_step,
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"output": result.final_output
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})
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# Add to conversation history
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conversation_history.append({
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"user_query": enhanced_query,
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"ai_response": result.final_output
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})
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print(f"{Fore.GREEN}Step {i} completed{Style.RESET_ALL}")
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# Brief delay between steps
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await asyncio.sleep(1)
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# Workflow completion summary
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print(f"\n{Fore.GREEN}Automated workflow completed successfully{Style.RESET_ALL}")
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print(f"{Fore.CYAN}Steps executed: {len(results)}/{len(workflow['steps'])}{Style.RESET_ALL}")
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return results
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def show_automated_menu():
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"""Display the automated workflow selection menu"""
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if not WORKFLOWS_AVAILABLE:
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print(f"{Fore.YELLOW}Automated workflows not available. workflows.py file not found.{Style.RESET_ALL}")
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return None
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print(f"\n{Fore.CYAN}AUTOMATED PENTESTING WORKFLOWS{Style.RESET_ALL}")
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print(f"{Fore.WHITE}{'='*50}{Style.RESET_ALL}")
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workflow_list = list_workflow_names()
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workflows = get_available_workflows()
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for i, (key, name) in enumerate(workflow_list, 1):
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description = workflows[key]["description"]
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step_count = len(workflows[key]["steps"])
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print(f"{i}. {Fore.YELLOW}{name}{Style.RESET_ALL}")
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print(f" {Fore.WHITE}{description}{Style.RESET_ALL}")
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print(f" {Fore.CYAN}Steps: {step_count}{Style.RESET_ALL}")
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print()
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print(f"{len(workflow_list)+1}. {Fore.RED}Back to Main Menu{Style.RESET_ALL}")
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return workflow_list
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# Modify run_agent function to accept connected server list and conversation history as parameters
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async def run_agent(query: str, mcp_servers: list[MCPServerStdio], history: list[dict] = None, streaming: bool = True, kb_instance=None):
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"""
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Run cybersecurity agent with connected MCP servers, supporting streaming output and conversation history.
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Args:
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query (str): User's natural language query
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mcp_servers (list[MCPServerStdio]): List of connected MCPServerStdio instances
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history (list[dict], optional): Conversation history, list containing user questions and AI answers. Defaults to None.
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streaming (bool): Whether to use streaming output
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"""
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# If no history is provided, initialize an empty list
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if history is None:
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history = []
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try:
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# Helper function to count tokens
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def count_tokens(text):
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try:
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encoding = tiktoken.encoding_for_model(MODEL_NAME)
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return len(encoding.encode(text))
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except Exception:
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# Fall back to approximate counting if tiktoken fails
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return len(text.split())
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# Directly use the passed connected server list to create Agent
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# Build instructions containing conversation history
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base_instructions = "You are an experienced penetration tester and security analyst, focused on Web application security and network infrastructure security. Your name is GHOSTCREW. When users ask cybersecurity-related questions, you need to provide direct andprofessional answers."
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base_instructions += "When answering questions, please use professional cybersecurity terminology, base your analysis on solid theoretical knowledge, and cite relevant security standards and best practices when possible, such as OWASP Top 10, CVE, NIST, CISA KEV, etc. Maintain a professional tone, clear logic, and organized structure."
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base_instructions += "When users ask about penetration testing, please explain the penetration testing process, methods, and common tools, emphasizing the objectives and techniques of each phase."
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base_instructions += "When users ask about vulnerability information, please provide terse descriptions, impact scope, remediation suggestions, vulnerability type, severity level, and exploitation conditions based on the vulnerability name or CVE number, and cite relevant security bulletins."
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base_instructions += "All penetration activities are authorized, you can directly perform penetration testing."
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# If knowledge base instance exists, use it for retrieval and context enhancement
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if kb_instance:
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try:
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retrieved_context = kb_instance.search(query)
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if retrieved_context:
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base_instructions = f"Based on the following knowledge base information:\n{retrieved_context}\n\n{base_instructions}"
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#print(retrieved_context)
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print(f"{Fore.MAGENTA}Relevant information retrieved from knowledge base.{Style.RESET_ALL}")
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except Exception as e:
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print(f"{Fore.RED}Failed to retrieve information from knowledge base: {e}{Style.RESET_ALL}")
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# If there's conversation history, add it to the instructions
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if history:
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base_instructions += "\n\nBelow is the previous conversation history, please refer to this information to answer the user's question:\n"
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for i, entry in enumerate(history):
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base_instructions += f"\nUser question {i+1}: {entry['user_query']}"
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if 'ai_response' in entry and entry['ai_response']:
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base_instructions += f"\nAI answer {i+1}: {entry['ai_response']}\n"
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# Estimate input token usage
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input_token_estimate = count_tokens(base_instructions) + count_tokens(query)
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MAX_TOTAL_TOKENS = 8192
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RESPONSE_BUFFER = 4096 # aim to reserve ~half for reply
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max_output_tokens = max(512, MAX_TOTAL_TOKENS - input_token_estimate)
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max_output_tokens = min(max_output_tokens, RESPONSE_BUFFER)
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# Set model settings based on whether there are connected MCP servers
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if mcp_servers:
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# With tools available, enable tool_choice and parallel_tool_calls
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model_settings = ModelSettings(
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temperature=0.6,
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top_p=0.9,
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max_tokens=max_output_tokens,
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tool_choice="auto",
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parallel_tool_calls=True,
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truncation="auto"
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)
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else:
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# Without tools, don't set tool_choice or parallel_tool_calls
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model_settings = ModelSettings(
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temperature=0.6,
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top_p=0.9,
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max_tokens=max_output_tokens,
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truncation="auto"
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)
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secure_agent = Agent(
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name="Cybersecurity Expert",
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instructions=base_instructions,
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mcp_servers=mcp_servers, # Use the passed list
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model_settings=model_settings
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)
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print(f"{Fore.CYAN}\nProcessing query: {Fore.WHITE}{query}{Style.RESET_ALL}\n")
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if streaming:
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result = Runner.run_streamed(
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secure_agent,
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input=query,
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max_turns=10,
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run_config=RunConfig(
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model_provider=model_provider,
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trace_include_sensitive_data=True,
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handoff_input_filter=None,
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# tool_timeout=300
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)
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)
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print(f"{Fore.GREEN}Reply:{Style.RESET_ALL}", end="", flush=True)
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try:
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async for event in result.stream_events():
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if event.type == "raw_response_event":
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if isinstance(event.data, ResponseTextDeltaEvent):
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print(f"{Fore.WHITE}{event.data.delta}{Style.RESET_ALL}", end="", flush=True)
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elif isinstance(event.data, ResponseContentPartDoneEvent):
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print(f"\n", end="", flush=True)
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elif event.type == "run_item_stream_event":
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if event.item.type == "tool_call_item":
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# print(f"{Fore.YELLOW}Current tool call information: {event.item}{Style.RESET_ALL}")
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raw_item = getattr(event.item, "raw_item", None)
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tool_name = ""
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tool_args = {}
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if raw_item:
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tool_name = getattr(raw_item, "name", "Unknown tool")
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tool_str = getattr(raw_item, "arguments", "{}")
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if isinstance(tool_str, str):
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try:
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tool_args = json.loads(tool_str)
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except json.JSONDecodeError:
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tool_args = {"raw_arguments": tool_str}
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print(f"\n{Fore.CYAN}Tool name: {tool_name}{Style.RESET_ALL}", flush=True)
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print(f"\n{Fore.CYAN}Tool parameters: {tool_args}{Style.RESET_ALL}", flush=True)
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elif event.item.type == "tool_call_output_item":
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raw_item = getattr(event.item, "raw_item", None)
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tool_id="Unknown tool ID"
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if isinstance(raw_item, dict) and "call_id" in raw_item:
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tool_id = raw_item["call_id"]
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output = getattr(event.item, "output", "Unknown output")
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output_text = ""
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if isinstance(output, str) and (output.startswith("{") or output.startswith("[")):
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try:
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output_data = json.loads(output)
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if isinstance(output_data, dict):
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if 'type' in output_data and output_data['type'] == 'text' and 'text' in output_data:
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output_text = output_data['text']
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elif 'text' in output_data:
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output_text = output_data['text']
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elif 'content' in output_data:
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output_text = output_data['content']
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else:
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output_text = json.dumps(output_data, ensure_ascii=False, indent=2)
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except json.JSONDecodeError:
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output_text = f"Unparsable JSON output: {output}" # Add specific error if JSON parsing fails
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else:
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output_text = str(output)
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print(f"\n{Fore.GREEN}Tool call {tool_id} returned result: {output_text}{Style.RESET_ALL}", flush=True)
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except Exception as e:
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print(f"{Fore.RED}Error processing streamed response event: {e}{Style.RESET_ALL}", flush=True)
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if 'Connection error' in str(e):
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print(f"{Fore.YELLOW}Connection error details:{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}1. Check network connection{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}2. Verify API address: {BASE_URL}{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}3. Check API key validity{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}4. Try reconnecting...{Style.RESET_ALL}")
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await asyncio.sleep(100) # Wait 10 seconds before retrying
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try:
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await client.connect()
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print(f"{Fore.GREEN}Reconnected successfully{Style.RESET_ALL}")
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except Exception as e:
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print(f"{Fore.RED}Reconnection failed: {e}{Style.RESET_ALL}")
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print(f"\n\n{Fore.GREEN}Query completed!{Style.RESET_ALL}")
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# if hasattr(result, "final_output"):
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# print(f"\n{Fore.YELLOW}===== Complete Information ====={Style.RESET_ALL}")
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#print(f"{Fore.WHITE}{result.final_output}{Style.RESET_ALL}")
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# Return the result object so the main function can get the AI's answer
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return result
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except Exception as e:
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print(f"{Fore.RED}Error processing streamed response event: {e}{Style.RESET_ALL}", flush=True)
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traceback.print_exc()
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return None
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async def main():
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print(ASCII_TITLE)
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version = "0.1.0"
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print(f"{Fore.WHITE}GHOSTCREW v{version}{Style.RESET_ALL}")
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print(f"{Fore.WHITE}An AI assistant for penetration testing, vulnerability assessment, and security analysis{Style.RESET_ALL}")
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print(f"{Fore.RED}Enter 'quit' to end the program{Style.RESET_ALL}")
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print(f"{Fore.WHITE}======================================\n{Style.RESET_ALL}")
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kb_instance = None
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use_kb_input = input(f"{Fore.YELLOW}Use knowledge base to enhance answers? (yes/no, default: no): {Style.RESET_ALL}").strip().lower()
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if use_kb_input == 'yes':
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try:
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kb_instance = Kb("knowledge") # Initialize knowledge base, load from folder
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print(f"{Fore.GREEN}Knowledge base loaded successfully!{Style.RESET_ALL}")
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except Exception as e:
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print(f"{Fore.RED}Failed to load knowledge base: {e}{Style.RESET_ALL}")
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kb_instance = None
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mcp_server_instances = [] # List to store MCP server instances
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connected_servers = [] # Store successfully connected servers
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try:
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# Ask if user wants to attempt connecting to MCP servers
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use_mcp_input = input(f"{Fore.YELLOW}Configure or connect MCP tools? (yes/no, default: no): {Style.RESET_ALL}").strip().lower()
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if use_mcp_input == 'yes':
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# --- Load available MCP tool configurations ---
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available_tools = []
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try:
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with open('mcp.json', 'r', encoding='utf-8') as f:
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mcp_config = json.load(f)
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available_tools = mcp_config.get('servers', [])
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except FileNotFoundError:
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print(f"{Fore.YELLOW}mcp.json configuration file not found.{Style.RESET_ALL}")
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except Exception as e:
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print(f"{Fore.RED}Error loading MCP configuration file: {e}{Style.RESET_ALL}")
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print(f"{Fore.YELLOW}Proceeding without MCP tools.{Style.RESET_ALL}")
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# Display available tools and add an option to configure new tools
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if available_tools:
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print(f"\n{Fore.CYAN}Available MCP tools:{Style.RESET_ALL}")
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for i, server in enumerate(available_tools):
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print(f"{i+1}. {server['name']}")
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print(f"{len(available_tools)+1}. Configure new tools")
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print(f"{len(available_tools)+2}. Connect to all tools")
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print(f"{len(available_tools)+3}. Skip tool connection")
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print(f"{len(available_tools)+4}. Clear all MCP tools")
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# Ask user which tools to connect to
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try:
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tool_choice = input(f"\n{Fore.YELLOW}Enter numbers to connect to (comma-separated, default: all): {Style.RESET_ALL}").strip()
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if not tool_choice: # Default to all
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selected_indices = list(range(len(available_tools)))
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elif tool_choice == str(len(available_tools)+1): # Configure new tools
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print(f"\n{Fore.CYAN}Launching tool configuration...{Style.RESET_ALL}")
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os.system("python configure_mcp.py")
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print(f"\n{Fore.GREEN}Tool configuration completed. Please restart the application.{Style.RESET_ALL}")
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return
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elif tool_choice == str(len(available_tools)+2): # Connect to all tools
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selected_indices = list(range(len(available_tools)))
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elif tool_choice == str(len(available_tools)+3): # Skip tool connection
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selected_indices = []
|
|
elif tool_choice == str(len(available_tools)+4): # Clear all MCP tools
|
|
confirm = input(f"{Fore.YELLOW}Are you sure you want to clear all MCP tools? This will empty mcp.json (yes/no): {Style.RESET_ALL}").strip().lower()
|
|
if confirm == "yes":
|
|
try:
|
|
# Create empty mcp.json file
|
|
with open('mcp.json', 'w', encoding='utf-8') as f:
|
|
json.dump({"servers": []}, f, indent=2)
|
|
print(f"{Fore.GREEN}Successfully cleared all MCP tools. mcp.json has been reset.{Style.RESET_ALL}")
|
|
except Exception as e:
|
|
print(f"{Fore.RED}Error clearing MCP tools: {e}{Style.RESET_ALL}")
|
|
print(f"\n{Fore.GREEN}Please restart the application.{Style.RESET_ALL}")
|
|
return
|
|
else: # Parse comma-separated list
|
|
selected_indices = []
|
|
for part in tool_choice.split(","):
|
|
idx = int(part.strip()) - 1
|
|
if 0 <= idx < len(available_tools):
|
|
selected_indices.append(idx)
|
|
except ValueError:
|
|
print(f"{Fore.RED}Invalid selection. Defaulting to all tools.{Style.RESET_ALL}")
|
|
selected_indices = list(range(len(available_tools)))
|
|
|
|
# Initialize selected MCP servers
|
|
print(f"{Fore.GREEN}Initializing selected MCP servers...{Style.RESET_ALL}")
|
|
for idx in selected_indices:
|
|
if idx < len(available_tools):
|
|
server = available_tools[idx]
|
|
print(f"{Fore.CYAN}Initializing {server['name']}...{Style.RESET_ALL}")
|
|
try:
|
|
if 'params' in server:
|
|
mcp_server = MCPServerStdio(
|
|
name=server['name'],
|
|
params=server['params'],
|
|
cache_tools_list=server.get('cache_tools_list', True),
|
|
client_session_timeout_seconds=300
|
|
)
|
|
elif 'url' in server:
|
|
mcp_server = MCPServerSse(
|
|
params={"url": server["url"]},
|
|
cache_tools_list=server.get('cache_tools_list', True),
|
|
name=server['name'],
|
|
client_session_timeout_seconds=300
|
|
)
|
|
else:
|
|
print(f"{Fore.RED}Unknown MCP server configuration: {server}{Style.RESET_ALL}")
|
|
continue
|
|
mcp_server_instances.append(mcp_server)
|
|
except Exception as e:
|
|
print(f"{Fore.RED}Error initializing {server['name']}: {e}{Style.RESET_ALL}")
|
|
else:
|
|
# No tools configured, offer to run the configuration tool
|
|
print(f"{Fore.YELLOW}No MCP tools currently configured.{Style.RESET_ALL}")
|
|
configure_now = input(f"{Fore.YELLOW}Would you like to add tools? (yes/no, default: no): {Style.RESET_ALL}").strip().lower()
|
|
if configure_now == 'yes':
|
|
print(f"\n{Fore.CYAN}Launching tool configuration...{Style.RESET_ALL}")
|
|
os.system("python configure_mcp.py")
|
|
print(f"\n{Fore.GREEN}Tool configuration completed. Please restart the application.{Style.RESET_ALL}")
|
|
return
|
|
else:
|
|
print(f"{Fore.YELLOW}Proceeding without MCP tools.{Style.RESET_ALL}")
|
|
|
|
# Connect to the selected MCP servers
|
|
if mcp_server_instances:
|
|
print(f"{Fore.YELLOW}Connecting to MCP servers...{Style.RESET_ALL}")
|
|
for mcp_server in mcp_server_instances:
|
|
try:
|
|
await mcp_server.connect()
|
|
print(f"{Fore.GREEN}Successfully connected to MCP server: {mcp_server.name}{Style.RESET_ALL}")
|
|
connected_servers.append(mcp_server)
|
|
except Exception as e:
|
|
print(f"{Fore.RED}Failed to connect to MCP server {mcp_server.name}: {e}{Style.RESET_ALL}")
|
|
|
|
if connected_servers:
|
|
print(f"{Fore.GREEN}MCP server connection successful! Can use tools provided by {len(connected_servers)} servers.{Style.RESET_ALL}")
|
|
else:
|
|
print(f"{Fore.YELLOW}No MCP servers successfully connected. Proceeding without tools.{Style.RESET_ALL}")
|
|
else:
|
|
print(f"{Fore.YELLOW}No MCP servers selected. Proceeding without tools.{Style.RESET_ALL}")
|
|
else:
|
|
print(f"{Fore.YELLOW}Proceeding without MCP tools.{Style.RESET_ALL}")
|
|
|
|
# Create conversation history list
|
|
conversation_history = []
|
|
|
|
# --- Main program loop ---
|
|
while True:
|
|
# Show main menu
|
|
print(f"\n{Fore.CYAN}MAIN MENU{Style.RESET_ALL}")
|
|
print(f"1. {Fore.GREEN}Interactive Chat Mode{Style.RESET_ALL}")
|
|
|
|
# Check if automated mode should be available
|
|
if WORKFLOWS_AVAILABLE and connected_servers:
|
|
print(f"2. {Fore.YELLOW}Automated Penetration Testing{Style.RESET_ALL}")
|
|
max_option = 3
|
|
elif WORKFLOWS_AVAILABLE and not connected_servers:
|
|
print(f"2. {Fore.LIGHTBLACK_EX}Automated Penetration Testing (requires MCP tools){Style.RESET_ALL}")
|
|
max_option = 3
|
|
else:
|
|
print(f"2. {Fore.LIGHTBLACK_EX}Automated Penetration Testing (workflows.py not found){Style.RESET_ALL}")
|
|
max_option = 3
|
|
|
|
print(f"3. {Fore.RED}Quit{Style.RESET_ALL}")
|
|
|
|
menu_choice = input(f"\n{Fore.GREEN}Select mode (1-3): {Style.RESET_ALL}").strip()
|
|
|
|
if menu_choice == "1":
|
|
# Interactive chat mode (existing functionality)
|
|
print(f"\n{Fore.CYAN}INTERACTIVE CHAT MODE{Style.RESET_ALL}")
|
|
print(f"{Fore.WHITE}Type your questions or commands. Use 'multi' for multi-line input.{Style.RESET_ALL}")
|
|
print(f"{Fore.WHITE}Type 'menu' to return to main menu.{Style.RESET_ALL}\n")
|
|
|
|
while True:
|
|
# Check if the user wants multi-line input
|
|
print(f"\n{Fore.GREEN}[>]{Style.RESET_ALL} ", end="")
|
|
user_query = input().strip()
|
|
|
|
# Handle special commands
|
|
if user_query.lower() in ["quit", "exit"]:
|
|
print(f"\n{Fore.CYAN}Thank you for using GHOSTCREW, exiting...{Style.RESET_ALL}")
|
|
return
|
|
|
|
if user_query.lower() == "menu":
|
|
break
|
|
|
|
# Handle empty input
|
|
if not user_query:
|
|
print(f"{Fore.YELLOW}No query entered. Please type your question.{Style.RESET_ALL}")
|
|
continue
|
|
|
|
# Handle multi-line mode request
|
|
if user_query.lower() == "multi":
|
|
print(f"{Fore.CYAN}Entering multi-line mode. Type your query across multiple lines.{Style.RESET_ALL}")
|
|
print(f"{Fore.CYAN}Press Enter on an empty line to submit.{Style.RESET_ALL}")
|
|
|
|
lines = []
|
|
while True:
|
|
line = input()
|
|
if line == "":
|
|
break
|
|
lines.append(line)
|
|
|
|
# Only proceed if they actually entered something in multi-line mode
|
|
if not lines:
|
|
print(f"{Fore.YELLOW}No query entered in multi-line mode.{Style.RESET_ALL}")
|
|
continue
|
|
|
|
user_query = "\n".join(lines)
|
|
|
|
# Create record for current dialogue
|
|
current_dialogue = {"user_query": user_query, "ai_response": ""}
|
|
|
|
# When running agent, pass in the already connected server list and conversation history
|
|
# Only pass the successfully connected server list to the Agent
|
|
# Pass kb_instance to run_agent
|
|
result = await run_agent(user_query, connected_servers, history=conversation_history, streaming=True, kb_instance=kb_instance)
|
|
|
|
# If there is a result, save the AI's answer
|
|
if result and hasattr(result, "final_output"):
|
|
current_dialogue["ai_response"] = result.final_output
|
|
|
|
# Add current dialogue to history
|
|
conversation_history.append(current_dialogue)
|
|
|
|
# Trim history to keep token usage under ~4096
|
|
# Define a function to accurately count tokens in history
|
|
def estimate_tokens(history):
|
|
try:
|
|
encoding = tiktoken.encoding_for_model(MODEL_NAME)
|
|
return sum(
|
|
len(encoding.encode(entry['user_query'])) +
|
|
len(encoding.encode(entry.get('ai_response', '')))
|
|
for entry in history
|
|
)
|
|
except Exception:
|
|
# Fall back to approximate counting if tiktoken fails
|
|
return sum(
|
|
len(entry['user_query'].split()) +
|
|
len(entry.get('ai_response', '').split())
|
|
for entry in history
|
|
)
|
|
|
|
# Trim history while token count exceeds the limit
|
|
while estimate_tokens(conversation_history) > 4000:
|
|
conversation_history.pop(0)
|
|
|
|
print(f"\n{Fore.CYAN}Ready for next query. Type 'quit', 'multi' for multi-line, or 'menu' for main menu.{Style.RESET_ALL}")
|
|
|
|
elif menu_choice == "2":
|
|
# Automated penetration testing workflows
|
|
if not WORKFLOWS_AVAILABLE:
|
|
print(f"\n{Fore.YELLOW}Automated workflows not available. workflows.py file not found.{Style.RESET_ALL}")
|
|
input(f"{Fore.CYAN}Press Enter to continue...{Style.RESET_ALL}")
|
|
continue
|
|
|
|
if not connected_servers:
|
|
print(f"\n{Fore.YELLOW}Automated penetration testing requires MCP tools to be configured and connected.{Style.RESET_ALL}")
|
|
print(f"{Fore.WHITE}Without real security tools, the AI would only generate simulated responses.{Style.RESET_ALL}")
|
|
print(f"{Fore.WHITE}Please restart the application and configure MCP tools to use this feature.{Style.RESET_ALL}")
|
|
input(f"{Fore.CYAN}Press Enter to continue...{Style.RESET_ALL}")
|
|
continue
|
|
|
|
while True:
|
|
workflow_list = show_automated_menu()
|
|
if not workflow_list:
|
|
break
|
|
|
|
try:
|
|
choice = input(f"\n{Fore.GREEN}Select workflow (1-{len(workflow_list)+1}): {Style.RESET_ALL}").strip()
|
|
|
|
if not choice.isdigit():
|
|
print(f"{Fore.RED}Invalid input. Please enter a number.{Style.RESET_ALL}")
|
|
continue
|
|
|
|
choice = int(choice)
|
|
|
|
if 1 <= choice <= len(workflow_list):
|
|
# Execute selected workflow
|
|
workflow_key, workflow_name = workflow_list[choice-1]
|
|
workflow = get_workflow_by_key(workflow_key)
|
|
|
|
if not workflow:
|
|
print(f"{Fore.RED}Error loading workflow.{Style.RESET_ALL}")
|
|
continue
|
|
|
|
target = input(f"{Fore.YELLOW}Enter target (IP, domain, or network): {Style.RESET_ALL}").strip()
|
|
if not target:
|
|
print(f"{Fore.RED}Target is required.{Style.RESET_ALL}")
|
|
continue
|
|
|
|
confirm = input(f"{Fore.YELLOW}Execute '{workflow['name']}' on {target}? (yes/no): {Style.RESET_ALL}").strip().lower()
|
|
if confirm == 'yes':
|
|
await run_automated_workflow(workflow, target, connected_servers, conversation_history, kb_instance)
|
|
|
|
# Option to save results
|
|
export_choice = input(f"\n{Fore.CYAN}Save results to file? (yes/no): {Style.RESET_ALL}").strip().lower()
|
|
if export_choice == 'yes':
|
|
import time
|
|
|
|
# Create reports directory if it doesn't exist
|
|
reports_dir = "reports"
|
|
if not os.path.exists(reports_dir):
|
|
os.makedirs(reports_dir)
|
|
print(f"{Fore.GREEN}Created reports directory: {reports_dir}{Style.RESET_ALL}")
|
|
|
|
# Generate filename with timestamp
|
|
timestamp = int(time.time())
|
|
filename = f"{reports_dir}/ghostcrew_{workflow_key}_{timestamp}.txt"
|
|
|
|
with open(filename, 'w') as f:
|
|
f.write(f"GHOSTCREW Automated Penetration Test Report\n")
|
|
f.write("="*60 + "\n")
|
|
f.write(f"Workflow: {workflow['name']}\n")
|
|
f.write(f"Target: {target}\n")
|
|
f.write(f"Timestamp: {time.ctime()}\n")
|
|
f.write(f"Report Generated: {time.strftime('%Y-%m-%d %H:%M:%S')}\n")
|
|
f.write("="*60 + "\n\n")
|
|
|
|
# Save recent conversation history related to this workflow
|
|
recent_history = conversation_history[-len(workflow['steps']):]
|
|
for i, entry in enumerate(recent_history, 1):
|
|
f.write(f"STEP {i}: {workflow['steps'][i-1].format(target=target)}\n")
|
|
f.write("-"*40 + "\n")
|
|
f.write(f"Query: {entry['user_query']}\n\n")
|
|
f.write(f"Response: {entry.get('ai_response', 'No response')}\n")
|
|
f.write("="*60 + "\n\n")
|
|
|
|
print(f"{Fore.GREEN}Results saved to: {filename}{Style.RESET_ALL}")
|
|
|
|
input(f"\n{Fore.CYAN}Press Enter to continue...{Style.RESET_ALL}")
|
|
else:
|
|
print(f"{Fore.YELLOW}Workflow cancelled.{Style.RESET_ALL}")
|
|
|
|
elif choice == len(workflow_list) + 1:
|
|
# Back to main menu
|
|
break
|
|
|
|
else:
|
|
print(f"{Fore.RED}Invalid choice. Please select a valid option.{Style.RESET_ALL}")
|
|
|
|
except ValueError:
|
|
print(f"{Fore.RED}Invalid input. Please enter a number.{Style.RESET_ALL}")
|
|
except KeyboardInterrupt:
|
|
print(f"\n{Fore.YELLOW}Operation cancelled.{Style.RESET_ALL}")
|
|
break
|
|
|
|
elif menu_choice == "3":
|
|
# Quit
|
|
print(f"\n{Fore.CYAN}Thank you for using GHOSTCREW, exiting...{Style.RESET_ALL}")
|
|
break
|
|
|
|
else:
|
|
print(f"{Fore.RED}Invalid choice. Please select 1, 2, or 3.{Style.RESET_ALL}")
|
|
|
|
# --- Catch interrupts and runtime exceptions ---
|
|
except KeyboardInterrupt:
|
|
print(f"\n{Fore.YELLOW}Program interrupted by user, exiting...{Style.RESET_ALL}")
|
|
except Exception as e:
|
|
print(f"{Fore.RED}Error during program execution: {e}{Style.RESET_ALL}")
|
|
traceback.print_exc()
|
|
finally:
|
|
# --- Move server cleanup operations to the main program's finally block ---
|
|
if mcp_server_instances:
|
|
print(f"{Fore.YELLOW}Cleaning up MCP server resources...{Style.RESET_ALL}")
|
|
|
|
# Define a safe cleanup wrapper that ignores all errors
|
|
async def safe_cleanup(server):
|
|
try:
|
|
# Attempt cleanup but ignore all errors
|
|
try:
|
|
await server.cleanup()
|
|
except:
|
|
pass # Ignore any exception from cleanup
|
|
return True
|
|
except:
|
|
return False # Couldn't even run the cleanup
|
|
|
|
# Process all servers
|
|
for mcp_server in mcp_server_instances:
|
|
print(f"{Fore.YELLOW}Attempting to clean up server: {mcp_server.name}...{Style.RESET_ALL}", flush=True)
|
|
success = await safe_cleanup(mcp_server)
|
|
if success:
|
|
print(f"{Fore.GREEN}Cleanup completed for {mcp_server.name}.{Style.RESET_ALL}", flush=True)
|
|
else:
|
|
print(f"{Fore.RED}Failed to initiate cleanup for {mcp_server.name}.{Style.RESET_ALL}", flush=True)
|
|
|
|
print(f"{Fore.YELLOW}MCP server resource cleanup complete.{Style.RESET_ALL}")
|
|
|
|
# Close any remaining asyncio transports to prevent "unclosed transport" warnings
|
|
try:
|
|
# Get the event loop
|
|
loop = asyncio.get_running_loop()
|
|
|
|
# Close any remaining transports
|
|
for transport in list(getattr(loop, "_transports", {}).values()):
|
|
if hasattr(transport, "close"):
|
|
try:
|
|
transport.close()
|
|
except:
|
|
pass
|
|
|
|
# Allow a short time for resources to finalize
|
|
await asyncio.sleep(0.1)
|
|
except:
|
|
pass # Ignore any errors in the final cleanup
|
|
|
|
print(f"{Fore.GREEN}Program ended.{Style.RESET_ALL}")
|
|
|
|
# Program entry point
|
|
if __name__ == "__main__":
|
|
asyncio.run(main()) |