mirror of
https://github.com/CyberSecurityUP/NeuroSploit.git
synced 2026-03-19 08:44:13 +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
41 lines
1.4 KiB
Python
Executable File
41 lines
1.4 KiB
Python
Executable File
"""
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NeuroSploit v3 - Report Schemas
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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 pydantic import BaseModel, Field
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class ReportGenerate(BaseModel):
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"""Schema for generating a report"""
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scan_id: str = Field(..., description="Scan ID to generate report for")
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format: str = Field("html", description="Report format: html, pdf, json")
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title: Optional[str] = Field(None, description="Custom report title")
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include_executive_summary: bool = Field(True, description="Include executive summary")
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include_poc: bool = Field(True, description="Include proof of concept")
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include_remediation: bool = Field(True, description="Include remediation steps")
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preferred_provider: Optional[str] = Field(None, description="Preferred LLM provider for AI report generation")
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preferred_model: Optional[str] = Field(None, description="Preferred model for AI report generation")
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class ReportResponse(BaseModel):
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"""Schema for report response"""
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id: str
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scan_id: str
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title: Optional[str]
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format: str
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file_path: Optional[str]
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executive_summary: Optional[str]
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auto_generated: bool = False
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is_partial: bool = False
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generated_at: datetime
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class Config:
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from_attributes = True
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class ReportListResponse(BaseModel):
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"""Schema for list of reports"""
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reports: List[ReportResponse]
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total: int
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