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"""
Cyber-LLM Agent Orchestrator
Main orchestration engine for coordinating multi-agent red team operations.
Manages workflow execution, safety checks, and human-in-the-loop approvals.
Author: Muzan Sano
Email: [email protected]
"""
import json
import logging
import asyncio
import yaml
from typing import Dict, List, Any, Optional, Type
from dataclasses import dataclass
from datetime import datetime
from pathlib import Path
# Import agents
from .recon_agent import ReconAgent, ReconRequest
from .c2_agent import C2Agent, C2Request
from .post_exploit_agent import PostExploitAgent, PostExploitRequest
from .safety_agent import SafetyAgent, SafetyRequest
from .explainability_agent import ExplainabilityAgent, ExplainabilityRequest
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class OperationContext:
"""Context for red team operation."""
operation_id: str
target: str
objectives: List[str]
constraints: Dict[str, Any]
approval_required: bool = True
stealth_mode: bool = True
max_duration: int = 14400 # 4 hours
@dataclass
class AgentResult:
"""Result from agent execution."""
agent_name: str
success: bool
data: Dict[str, Any]
execution_time: float
risk_score: float
errors: List[str] = None
class RedTeamOrchestrator:
"""
Advanced orchestrator for coordinating multi-agent red team operations.
Implements safety checks, human approval workflows, and operational security.
"""
def __init__(self, config_path: Optional[str] = None):
self.config = self._load_config(config_path)
self.agents = self._initialize_agents()
self.workflows = self._load_workflows()
self.operation_history = []
def _load_config(self, config_path: Optional[str]) -> Dict[str, Any]:
"""Load orchestrator configuration."""
if config_path:
with open(config_path, 'r') as f:
return yaml.safe_load(f)
return {
"max_parallel_agents": 3,
"safety_threshold": 0.7,
"require_human_approval": True,
"log_all_operations": True,
"auto_cleanup": True
}
def _initialize_agents(self) -> Dict[str, Any]:
"""Initialize all available agents."""
return {
"recon": ReconAgent(),
"c2": C2Agent(),
"post_exploit": PostExploitAgent(),
"safety": SafetyAgent(),
"explainability": ExplainabilityAgent()
}
def _load_workflows(self) -> Dict[str, Any]:
"""Load predefined workflow templates."""
return {
"standard_red_team": {
"name": "Standard Red Team Assessment",
"description": "Full red team engagement workflow",
"phases": [
{
"name": "reconnaissance",
"agents": ["recon"],
"parallel": False,
"safety_check": True,
"human_approval": True
},
{
"name": "initial_access",
"agents": ["c2"],
"parallel": False,
"safety_check": True,
"human_approval": True,
"depends_on": ["reconnaissance"]
},
{
"name": "post_exploitation",
"agents": ["post_exploit"],
"parallel": False,
"safety_check": True,
"human_approval": True,
"depends_on": ["initial_access"]
}
]
},
"stealth_assessment": {
"name": "Stealth Red Team Assessment",
"description": "Low-detection red team workflow",
"phases": [
{
"name": "passive_recon",
"agents": ["recon"],
"parallel": False,
"safety_check": True,
"human_approval": False,
"config_overrides": {"scan_type": "passive"}
},
{
"name": "targeted_exploitation",
"agents": ["c2", "post_exploit"],
"parallel": True,
"safety_check": True,
"human_approval": True,
"depends_on": ["passive_recon"]
}
]
},
"credential_focused": {
"name": "Credential Harvesting Focus",
"description": "Credential-focused assessment workflow",
"phases": [
{
"name": "initial_recon",
"agents": ["recon"],
"parallel": False,
"safety_check": True,
"human_approval": False
},
{
"name": "credential_harvest",
"agents": ["post_exploit"],
"parallel": False,
"safety_check": True,
"human_approval": True,
"config_overrides": {"objectives": ["credential_harvest"]},
"depends_on": ["initial_recon"]
}
]
}
}
async def safety_check(self, agent_name: str, planned_actions: Dict[str, Any]) -> Dict[str, Any]:
"""
Perform safety and OPSEC compliance check.
# HUMAN_APPROVAL_REQUIRED: Safety checks require human oversight
"""
logger.info(f"Performing safety check for {agent_name}")
safety_agent = self.agents["safety"]
safety_result = await safety_agent.evaluate_actions(planned_actions)
# Check against safety threshold
if safety_result["risk_score"] > self.config["safety_threshold"]:
logger.warning(f"High risk detected for {agent_name}: {safety_result['risk_score']}")
safety_result["approved"] = False
safety_result["reason"] = "Risk score exceeds safety threshold"
else:
safety_result["approved"] = True
return safety_result
async def request_human_approval(self, agent_name: str, planned_actions: Dict[str, Any],
safety_result: Dict[str, Any]) -> bool:
"""
Request human approval for high-risk operations.
# HUMAN_APPROVAL_REQUIRED: This function handles human approval workflow
"""
print(f"\n{'='*60}")
print(f"HUMAN APPROVAL REQUIRED - {agent_name.upper()}")
print(f"{'='*60}")
print(f"Risk Score: {safety_result.get('risk_score', 'Unknown')}")
print(f"Risk Level: {safety_result.get('risk_level', 'Unknown')}")
if safety_result.get('risks'):
print("\nIdentified Risks:")
for risk in safety_result['risks']:
print(f" - {risk}")
if safety_result.get('mitigations'):
print("\nRecommended Mitigations:")
for mitigation in safety_result['mitigations']:
print(f" - {mitigation}")
print(f"\nPlanned Actions Summary:")
print(json.dumps(planned_actions, indent=2))
print(f"\n{'='*60}")
# In a real implementation, this would integrate with a proper approval system
while True:
response = input("Approve this operation? [y/N/details]: ").lower().strip()
if response in ['y', 'yes']:
logger.info(f"Human approval granted for {agent_name}")
return True
elif response in ['n', 'no', '']:
logger.info(f"Human approval denied for {agent_name}")
return False
elif response == 'details':
print("\nDetailed Action Plan:")
print(json.dumps(planned_actions, indent=2))
else:
print("Please enter 'y' for yes, 'n' for no, or 'details' for more information")
async def execute_agent(self, agent_name: str, context: OperationContext,
config_overrides: Optional[Dict[str, Any]] = None) -> AgentResult:
"""Execute a single agent with safety checks and approval workflow."""
start_time = datetime.now()
try:
agent = self.agents[agent_name]
# Create agent-specific request
if agent_name == "recon":
request = ReconRequest(
target=context.target,
scan_type=config_overrides.get("scan_type", "stealth") if config_overrides else "stealth",
stealth_mode=context.stealth_mode
)
planned_actions = {
"agent": agent_name,
"target": context.target,
"scan_type": request.scan_type
}
elif agent_name == "c2":
request = C2Request(
payload_type="powershell",
target_environment="corporate", # Could be derived from recon
network_constraints=context.constraints.get("network", {}),
stealth_level="high" if context.stealth_mode else "medium"
)
planned_actions = {
"agent": agent_name,
"payload_type": request.payload_type,
"stealth_level": request.stealth_level
}
elif agent_name == "post_exploit":
request = PostExploitRequest(
target_system=context.target,
access_level="user", # Could be updated based on previous results
objectives=config_overrides.get("objectives", context.objectives) if config_overrides else context.objectives,
constraints=context.constraints,
stealth_mode=context.stealth_mode
)
planned_actions = {
"agent": agent_name,
"target": context.target,
"objectives": request.objectives
}
else:
raise ValueError(f"Unknown agent: {agent_name}")
# Safety check
if context.approval_required:
safety_result = await self.safety_check(agent_name, planned_actions)
if not safety_result["approved"]:
return AgentResult(
agent_name=agent_name,
success=False,
data={"error": "Failed safety check", "safety_result": safety_result},
execution_time=0,
risk_score=safety_result.get("risk_score", 1.0),
errors=["Safety check failed"]
)
# Request human approval if required
if self.config["require_human_approval"]:
approved = await self.request_human_approval(agent_name, planned_actions, safety_result)
if not approved:
return AgentResult(
agent_name=agent_name,
success=False,
data={"error": "Human approval denied"},
execution_time=0,
risk_score=safety_result.get("risk_score", 1.0),
errors=["Human approval denied"]
)
# Execute agent
logger.info(f"Executing {agent_name} agent")
if agent_name == "recon":
result = agent.execute_reconnaissance(request)
elif agent_name == "c2":
result = agent.execute_c2_setup(request)
elif agent_name == "post_exploit":
result = agent.execute_post_exploitation(request)
execution_time = (datetime.now() - start_time).total_seconds()
# Extract risk score from result
risk_score = 0.0
if hasattr(result, 'risk_assessment') and result.risk_assessment:
risk_score = result.risk_assessment.get('risk_score', 0.0)
return AgentResult(
agent_name=agent_name,
success=True,
data=result.dict() if hasattr(result, 'dict') else result,
execution_time=execution_time,
risk_score=risk_score
)
except Exception as e:
execution_time = (datetime.now() - start_time).total_seconds()
logger.error(f"Error executing {agent_name}: {str(e)}")
return AgentResult(
agent_name=agent_name,
success=False,
data={"error": str(e)},
execution_time=execution_time,
risk_score=1.0,
errors=[str(e)]
)
async def execute_workflow(self, workflow_name: str, context: OperationContext) -> Dict[str, Any]:
"""
Execute a complete red team workflow.
# HUMAN_APPROVAL_REQUIRED: Workflow execution requires oversight
"""
if workflow_name not in self.workflows:
raise ValueError(f"Unknown workflow: {workflow_name}")
workflow = self.workflows[workflow_name]
logger.info(f"Starting workflow: {workflow['name']}")
operation_start = datetime.now()
results = {}
phase_results = {}
try:
for phase in workflow["phases"]:
phase_name = phase["name"]
logger.info(f"Executing phase: {phase_name}")
# Check dependencies
if "depends_on" in phase:
for dependency in phase["depends_on"]:
if dependency not in phase_results or not phase_results[dependency]["success"]:
logger.error(f"Phase {phase_name} dependency {dependency} not satisfied")
phase_results[phase_name] = {
"success": False,
"error": f"Dependency {dependency} not satisfied"
}
continue
# Execute agents in phase
if phase.get("parallel", False):
# Execute agents in parallel
tasks = []
for agent_name in phase["agents"]:
config_overrides = phase.get("config_overrides")
task = self.execute_agent(agent_name, context, config_overrides)
tasks.append(task)
agent_results = await asyncio.gather(*tasks)
else:
# Execute agents sequentially
agent_results = []
for agent_name in phase["agents"]:
config_overrides = phase.get("config_overrides")
result = await self.execute_agent(agent_name, context, config_overrides)
agent_results.append(result)
# Process phase results
phase_success = all(result.success for result in agent_results)
phase_results[phase_name] = {
"success": phase_success,
"agents": {result.agent_name: result for result in agent_results},
"execution_time": sum(result.execution_time for result in agent_results),
"max_risk_score": max(result.risk_score for result in agent_results) if agent_results else 0.0
}
# Update context with results for next phase
for result in agent_results:
if result.success and result.agent_name == "recon":
# Update context with reconnaissance findings
if "nmap" in result.data:
context.constraints["discovered_services"] = result.data.get("nmap", [])
logger.info(f"Phase {phase_name} completed: {'SUCCESS' if phase_success else 'FAILED'}")
except Exception as e:
logger.error(f"Workflow execution failed: {str(e)}")
phase_results["error"] = str(e)
# Generate final results
operation_time = (datetime.now() - operation_start).total_seconds()
overall_success = all(phase["success"] for phase in phase_results.values() if isinstance(phase, dict) and "success" in phase)
results = {
"operation_id": context.operation_id,
"workflow": workflow_name,
"target": context.target,
"success": overall_success,
"execution_time": operation_time,
"phases": phase_results,
"timestamp": operation_start.isoformat(),
"context": {
"objectives": context.objectives,
"stealth_mode": context.stealth_mode,
"approval_required": context.approval_required
}
}
# Store in operation history
self.operation_history.append(results)
logger.info(f"Workflow {workflow_name} completed: {'SUCCESS' if overall_success else 'FAILED'}")
return results
def generate_operation_report(self, operation_results: Dict[str, Any]) -> str:
"""Generate comprehensive operation report."""
explainability_agent = self.agents["explainability"]
return explainability_agent.generate_operation_report(operation_results)
async def cleanup_operation(self, operation_id: str):
"""Cleanup resources and artifacts from operation."""
logger.info(f"Cleaning up operation: {operation_id}")
# In a real implementation, this would:
# - Remove temporary files
# - Close network connections
# - Remove persistence mechanisms
# - Clear logs if required
logger.info(f"Cleanup completed for operation: {operation_id}")
def main():
"""CLI interface for Red Team Orchestrator."""
import argparse
import uuid
parser = argparse.ArgumentParser(description="Cyber-LLM Red Team Orchestrator")
parser.add_argument("--workflow", required=True, help="Workflow to execute")
parser.add_argument("--target", required=True, help="Target for assessment")
parser.add_argument("--objectives", nargs="+", default=["reconnaissance", "initial_access"],
help="Operation objectives")
parser.add_argument("--stealth", action="store_true", help="Enable stealth mode")
parser.add_argument("--no-approval", action="store_true", help="Skip human approval")
parser.add_argument("--config", help="Path to configuration file")
parser.add_argument("--output", help="Output file for results")
args = parser.parse_args()
async def run_operation():
# Initialize orchestrator
orchestrator = RedTeamOrchestrator(config_path=args.config)
# Create operation context
context = OperationContext(
operation_id=str(uuid.uuid4()),
target=args.target,
objectives=args.objectives,
constraints={},
approval_required=not args.no_approval,
stealth_mode=args.stealth
)
# Execute workflow
results = await orchestrator.execute_workflow(args.workflow, context)
# Generate report
report = orchestrator.generate_operation_report(results)
# Output results
output_data = {
"results": results,
"report": report
}
if args.output:
with open(args.output, 'w') as f:
json.dump(output_data, f, indent=2)
print(f"Operation results saved to {args.output}")
else:
print(json.dumps(output_data, indent=2))
# Cleanup
await orchestrator.cleanup_operation(context.operation_id)
# Run the async operation
asyncio.run(run_operation())
if __name__ == "__main__":
main()
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