mirror of
https://github.com/CyberSecurityUP/NeuroSploit.git
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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
78 lines
2.0 KiB
Python
Executable File
78 lines
2.0 KiB
Python
Executable File
"""
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NeuroSploit v3 - Prompt 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 PromptCreate(BaseModel):
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"""Schema for creating a prompt"""
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name: str = Field(..., max_length=255, description="Prompt name")
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description: Optional[str] = Field(None, description="Prompt description")
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content: str = Field(..., min_length=10, description="Prompt content")
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category: Optional[str] = Field(None, description="Prompt category")
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class PromptUpdate(BaseModel):
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"""Schema for updating a prompt"""
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name: Optional[str] = None
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description: Optional[str] = None
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content: Optional[str] = None
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category: Optional[str] = None
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class PromptParse(BaseModel):
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"""Schema for parsing a prompt"""
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content: str = Field(..., min_length=10, description="Prompt content to parse")
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class VulnerabilityTypeExtracted(BaseModel):
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"""Extracted vulnerability type from prompt"""
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type: str
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category: str
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confidence: float
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context: Optional[str] = None
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class TestingScope(BaseModel):
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"""Testing scope extracted from prompt"""
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include_recon: bool = True
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depth: str = "standard" # quick, standard, thorough, exhaustive
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max_requests_per_endpoint: Optional[int] = None
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time_limit_minutes: Optional[int] = None
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class PromptParseResult(BaseModel):
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"""Result of prompt parsing"""
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vulnerabilities_to_test: List[VulnerabilityTypeExtracted]
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testing_scope: TestingScope
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special_instructions: List[str] = []
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target_filters: dict = {}
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output_preferences: dict = {}
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class PromptResponse(BaseModel):
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"""Schema for prompt response"""
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id: str
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name: str
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description: Optional[str]
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content: str
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is_preset: bool
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category: Optional[str]
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parsed_vulnerabilities: List
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created_at: datetime
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updated_at: datetime
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class Config:
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from_attributes = True
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class PromptPreset(BaseModel):
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"""Schema for preset prompt"""
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id: str
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name: str
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description: str
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category: str
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vulnerability_count: int
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