mirror of
https://github.com/CyberSecurityUP/NeuroSploit.git
synced 2026-03-20 09:13:37 +00:00
e0935793c5
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
62 lines
2.4 KiB
Python
Executable File
62 lines
2.4 KiB
Python
Executable File
"""
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NeuroSploit v3 - Endpoint Model
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"""
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from datetime import datetime
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from typing import Optional, List
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from sqlalchemy import String, Integer, DateTime, Text, JSON, ForeignKey
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from sqlalchemy.orm import Mapped, mapped_column, relationship
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from backend.db.database import Base
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import uuid
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class Endpoint(Base):
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"""Discovered endpoint model"""
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__tablename__ = "endpoints"
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id: Mapped[str] = mapped_column(String(36), primary_key=True, default=lambda: str(uuid.uuid4()))
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scan_id: Mapped[str] = mapped_column(String(36), ForeignKey("scans.id", ondelete="CASCADE"))
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target_id: Mapped[Optional[str]] = mapped_column(String(36), ForeignKey("targets.id", ondelete="SET NULL"), nullable=True)
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# Endpoint details
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url: Mapped[str] = mapped_column(Text)
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method: Mapped[str] = mapped_column(String(10), default="GET")
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path: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
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# Parameters
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parameters: Mapped[List] = mapped_column(JSON, default=list) # [{name, type, value}]
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headers: Mapped[dict] = mapped_column(JSON, default=dict)
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# Response info
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response_status: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
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content_type: Mapped[Optional[str]] = mapped_column(String(100), nullable=True)
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content_length: Mapped[Optional[int]] = mapped_column(Integer, nullable=True)
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# Detection
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technologies: Mapped[List] = mapped_column(JSON, default=list)
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interesting: Mapped[bool] = mapped_column(default=False) # Marked as interesting for testing
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# Timestamps
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discovered_at: Mapped[datetime] = mapped_column(DateTime, default=datetime.utcnow)
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# Relationships
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scan: Mapped["Scan"] = relationship("Scan", back_populates="endpoints")
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def to_dict(self) -> dict:
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"""Convert to dictionary"""
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return {
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"id": self.id,
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"scan_id": self.scan_id,
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"target_id": self.target_id,
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"url": self.url,
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"method": self.method,
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"path": self.path,
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"parameters": self.parameters,
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"headers": self.headers,
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"response_status": self.response_status,
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"content_type": self.content_type,
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"content_length": self.content_length,
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"technologies": self.technologies,
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"interesting": self.interesting,
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"discovered_at": self.discovered_at.isoformat() if self.discovered_at else None
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}
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