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NeuroSploit v3.2 - Autonomous AI Penetration Testing Platform
116 modules | 100 vuln types | 18 API routes | 18 frontend pages Major features: - VulnEngine: 100 vuln types, 526+ payloads, 12 testers, anti-hallucination prompts - Autonomous Agent: 3-stream auto pentest, multi-session (5 concurrent), pause/resume/stop - CLI Agent: Claude Code / Gemini CLI / Codex CLI inside Kali containers - Validation Pipeline: negative controls, proof of execution, confidence scoring, judge - AI Reasoning: ReACT engine, token budget, endpoint classifier, CVE hunter, deep recon - Multi-Agent: 5 specialists + orchestrator + researcher AI + vuln type agents - RAG System: BM25/TF-IDF/ChromaDB vectorstore, few-shot, reasoning templates - Smart Router: 20 providers (8 CLI OAuth + 12 API), tier failover, token refresh - Kali Sandbox: container-per-scan, 56 tools, VPN support, on-demand install - Full IA Testing: methodology-driven comprehensive pentest sessions - Notifications: Discord, Telegram, WhatsApp/Twilio multi-channel alerts - Frontend: React/TypeScript with 18 pages, real-time WebSocket updates
This commit is contained in:
CyberSecurityUP
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"""
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NeuroSploit v3 - Multi-Agent Orchestrator
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Coordinates specialist agents in a CAI-inspired pattern:
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Phase 1: Parallel — ReconAgent + CVEHunterAgent
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Phase 2: Sequential — ExploitAgent (consumes recon output)
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Phase 3: Parallel — ValidatorAgent + ReportAgent
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Manages handoffs, shared memory, and progress tracking.
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Enabled via ENABLE_MULTI_AGENT=true in .env.
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"""
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import asyncio
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import logging
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import time
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from typing import Any, Dict, List, Optional
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from core.agent_base import SpecialistAgent, AgentResult
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logger = logging.getLogger(__name__)
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# Lazy imports
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_specialist = None
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def _get_specialist_module():
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global _specialist
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if _specialist is None:
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try:
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from core import specialist_agents
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_specialist = specialist_agents
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except ImportError:
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_specialist = False
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return _specialist if _specialist else None
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class AgentOrchestrator:
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"""Coordinates specialist agents with handoff routing.
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Three execution phases:
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1. Intelligence gathering (Recon + CVE Hunter in parallel)
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2. Exploitation (ExploitAgent with enriched recon)
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3. Validation and reporting (Validator + Reporter in parallel)
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Handoff rules route output from one agent as input to the next.
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Shared memory allows agents to see each other's findings.
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"""
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def __init__(
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self,
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llm=None,
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memory=None,
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budget=None,
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request_engine=None,
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config: Dict = None,
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):
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self.llm = llm
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self.memory = memory
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self.budget = budget
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self.request_engine = request_engine
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self.config = config or {}
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self._agents: Dict[str, SpecialistAgent] = {}
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self._results: Dict[str, AgentResult] = {}
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self._phase = "idle"
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self._start_time: float = 0.0
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self._cancel_event = asyncio.Event()
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self._progress_callback = None
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self._init_agents()
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def _init_agents(self):
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"""Initialize specialist agents with budget allocations."""
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spec = _get_specialist_module()
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if not spec:
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logger.warning("Specialist agents module not available")
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return
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budget_splits = self.config.get("budget_splits", {
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"recon": 0.20,
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"exploit": 0.35,
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"validator": 0.20,
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"cve_hunter": 0.10,
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"reporter": 0.15,
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})
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self._agents = {
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"recon": spec.ReconAgent(
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llm=self.llm, memory=self.memory,
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budget_allocation=budget_splits.get("recon", 0.20),
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budget=self.budget, request_engine=self.request_engine,
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),
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"exploit": spec.ExploitAgent(
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llm=self.llm, memory=self.memory,
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budget_allocation=budget_splits.get("exploit", 0.35),
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budget=self.budget, request_engine=self.request_engine,
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),
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"validator": spec.ValidatorAgent(
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llm=self.llm, memory=self.memory,
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budget_allocation=budget_splits.get("validator", 0.20),
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budget=self.budget, request_engine=self.request_engine,
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),
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"cve_hunter": spec.CVEHunterAgent(
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llm=self.llm, memory=self.memory,
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budget_allocation=budget_splits.get("cve_hunter", 0.10),
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budget=self.budget, request_engine=self.request_engine,
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),
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"reporter": spec.ReportAgent(
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llm=self.llm, memory=self.memory,
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budget_allocation=budget_splits.get("reporter", 0.15),
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budget=self.budget,
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),
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}
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def set_progress_callback(self, callback):
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"""Set callback for progress updates: callback(phase, pct, message)."""
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self._progress_callback = callback
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def _progress(self, phase: str, pct: float, message: str):
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"""Report progress."""
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self._phase = phase
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if self._progress_callback:
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try:
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self._progress_callback(phase, pct, message)
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except Exception:
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pass
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async def run(
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self,
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target: str,
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recon_data: Any = None,
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initial_context: Dict = None,
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) -> Dict:
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"""Run the full multi-agent pipeline.
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Args:
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target: Target URL
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recon_data: Existing ReconData object (if available)
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initial_context: Additional context (headers, body, technologies)
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Returns:
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Dict with all findings, agent results, and statistics.
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"""
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self._start_time = time.time()
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self._cancel_event.clear()
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context = initial_context or {}
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context["target"] = target
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# Extract basic info from recon_data
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if recon_data:
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context.setdefault("headers", getattr(recon_data, "headers", {}))
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context.setdefault("body", getattr(recon_data, "body", ""))
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context.setdefault("technologies",
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getattr(recon_data, "technologies", []))
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context.setdefault("endpoints",
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getattr(recon_data, "endpoints", []))
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all_findings = []
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pipeline_results = {}
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# ── Phase 1: Intelligence Gathering (Parallel) ──
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self._progress("phase1_intel", 0.0, "Starting intelligence gathering")
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if self._cancel_event.is_set():
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return self._build_result(all_findings, pipeline_results)
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phase1_tasks = []
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if "recon" in self._agents:
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phase1_tasks.append(
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("recon", self._agents["recon"].execute(context))
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)
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if "cve_hunter" in self._agents:
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phase1_tasks.append(
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("cve_hunter", self._agents["cve_hunter"].execute(context))
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)
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if phase1_tasks:
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results = await asyncio.gather(
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*[task for _, task in phase1_tasks],
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return_exceptions=True,
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)
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for (name, _), res in zip(phase1_tasks, results):
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if isinstance(res, Exception):
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logger.error(f"Phase 1 agent {name} failed: {res}")
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pipeline_results[name] = AgentResult(
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agent_name=name, status="failed", error=str(res)
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)
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else:
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pipeline_results[name] = res
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all_findings.extend(res.findings)
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# Merge discovered endpoints into context
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if name == "recon" and res.data.get("discovered_endpoints"):
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existing = context.get("endpoints", [])
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context["endpoints"] = list(set(
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existing + res.data["discovered_endpoints"]
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))
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if name == "recon" and res.data.get("version_findings"):
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context["versions"] = res.data["version_findings"]
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self._progress("phase1_intel", 0.30,
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f"Intel complete: {len(context.get('endpoints', []))} endpoints")
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# ── Phase 2: Exploitation (Sequential) ──
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if self._cancel_event.is_set():
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return self._build_result(all_findings, pipeline_results)
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self._progress("phase2_exploit", 0.30, "Starting exploitation phase")
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if "exploit" in self._agents:
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exploit_result = await self._agents["exploit"].execute(context)
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pipeline_results["exploit"] = exploit_result
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all_findings.extend(exploit_result.findings)
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self._progress("phase2_exploit", 0.65,
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f"Exploitation complete: {len(all_findings)} findings")
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# ── Phase 3: Validation + Reporting (Parallel) ──
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if self._cancel_event.is_set():
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return self._build_result(all_findings, pipeline_results)
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self._progress("phase3_validate", 0.65, "Starting validation and reporting")
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phase3_context = {**context, "findings": all_findings}
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phase3_tasks = []
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if "validator" in self._agents and all_findings:
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phase3_tasks.append(
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("validator", self._agents["validator"].execute(phase3_context))
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)
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if "reporter" in self._agents and all_findings:
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report_ctx = {**phase3_context, "recon_data": recon_data}
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phase3_tasks.append(
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("reporter", self._agents["reporter"].execute(report_ctx))
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)
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if phase3_tasks:
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results = await asyncio.gather(
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*[task for _, task in phase3_tasks],
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return_exceptions=True,
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)
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for (name, _), res in zip(phase3_tasks, results):
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if isinstance(res, Exception):
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logger.error(f"Phase 3 agent {name} failed: {res}")
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pipeline_results[name] = AgentResult(
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agent_name=name, status="failed", error=str(res)
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)
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else:
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pipeline_results[name] = res
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# Validator may filter findings
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if name == "validator" and res.findings:
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all_findings = res.findings
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self._progress("complete", 1.0,
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f"Pipeline complete: {len(all_findings)} validated findings")
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return self._build_result(all_findings, pipeline_results)
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def _build_result(
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self,
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findings: List,
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agent_results: Dict[str, AgentResult],
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) -> Dict:
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"""Build final pipeline result."""
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elapsed = time.time() - self._start_time if self._start_time else 0
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total_tokens = sum(
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r.tokens_used for r in agent_results.values()
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if isinstance(r, AgentResult)
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)
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total_tasks = sum(
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r.tasks_completed for r in agent_results.values()
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if isinstance(r, AgentResult)
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)
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return {
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"findings": findings,
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"findings_count": len(findings),
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"agent_results": {
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name: {
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"status": r.status,
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"findings_count": len(r.findings),
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"tasks_completed": r.tasks_completed,
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"tokens_used": r.tokens_used,
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"duration": round(r.duration, 1),
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"error": r.error,
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}
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for name, r in agent_results.items()
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if isinstance(r, AgentResult)
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},
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"total_tokens": total_tokens,
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"total_tasks": total_tasks,
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"duration": round(elapsed, 1),
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"phase": self._phase,
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}
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def cancel(self):
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"""Cancel all running agents."""
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self._cancel_event.set()
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for agent in self._agents.values():
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agent.cancel()
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def get_agents_status(self) -> List[Dict]:
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"""Get status of all agents for dashboard."""
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return [agent.get_status() for agent in self._agents.values()]
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async def reason_about_handoff(
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self,
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current_agent: str,
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result: AgentResult,
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) -> Optional[str]:
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"""Use AI to decide which agent should handle next.
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Falls back to explicit handoff_to in AgentResult.
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"""
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if result.handoff_to:
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return result.handoff_to
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if not self.llm or not hasattr(self.llm, "generate"):
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return None
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try:
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prompt = f"""Given the output of the {current_agent} agent:
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- Status: {result.status}
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- Findings: {len(result.findings)}
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- Data keys: {list(result.data.keys())}
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Which agent should handle the next step?
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Options: recon, exploit, validator, cve_hunter, reporter, none
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Reply with ONLY the agent name."""
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answer = await self.llm.generate(prompt)
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if answer:
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answer = answer.strip().lower()
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if answer in self._agents:
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return answer
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except Exception:
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pass
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return None
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