sylhetyhackvenger/GOBLIN-Tsunami

## 📋 描述 **GOBLIN TSUNAMI** 是一个企业级的开源情报 (OSINT) 框架，专为全面的数字足迹分析和社交媒体情报收集而设计。该工具利用先进的 Google Dorking 技术，在四大主要平台（Instagram、Threads、Facebook 和 TikTok）上系统地生成 400 多条有针对性的搜索查询，以映射个人的完整数字足迹。该框架采用复杂的模式识别算法，提供涵盖 25 个以上不同活动维度的分类跟踪向量，包括地理位置数据、社交联系、职业从属关系、日常作息和行为模式。GOBLIN TSUNAMI 采用赛博朋克美学和模块化架构构建，代表了新一代的合规侦察工具，使网络安全专业人员、威胁分析师和数字调查员能够进行彻底的公开信息评估，同时严格遵守道德准则和法律框架。 ## 🚀 核心功能 ### 🎯 全面的平台覆盖 - **Instagram**：个人资料分析、帖子跟踪、位置映射、互动指标 - **Threads**：对话分析、回复跟踪、网络映射 - **Facebook**：资料情报、事件跟踪、群组成员分析 - **TikTok**：内容分析、病毒式趋势跟踪、声音/音乐识别 ### 🔍 高级跟踪类别 📍 位置情报 👥 社交网络分析 💼 职业档案 🎯 兴趣与行为映射 📱 数字足迹 ⏰ 时间活动模式 🔗 关系映射 📊 内容分析 🏢 组织关联 🌐 跨平台关联 ### 🛠️ 核心能力 - **400+ 自动化 Dork 生成**：针对每个目标、每个平台 - **25+ 跟踪维度**：全面的行为分析 - **多平台关联**：交叉比对社交媒体足迹 - **基于时间的分析**：历史足迹跟踪 - **文件类型发现**：媒体和文档情报 - **简介与链接提取**：数字面包屑收集 ## 🏗️ 架构与数字模拟器 ``` graph TB subgraph "Goblin Tsunami Architecture" A[User Interface Layer] --> B[Command Parser] B --> C[Dork Generation Engine] C --> D[Platform Modules] D --> E[Instagram Module] D --> F[Threads Module] D --> G[Facebook Module] D --> H[TikTok Module] E --> I[Location Tracker] E --> J[Activity Analyzer] E --> K[Content Extractor] F --> L[Conversation Analyzer] F --> M[Network Mapper] G --> N[Profile Scraper] G --> O[Event Tracker] H --> P[Trend Analyzer] H --> Q[Engagement Tracker] I --> R[Results Aggregator] J --> R K --> R L --> R M --> R N --> R O --> R P --> R Q --> R R --> S[Output Formatter] S --> T[Text File Export] S --> U[Console Display] S --> V[PDF Report] end subgraph "Data Processing Pipeline" W[Raw Search Queries] --> X[Query Optimization] X --> Y[Parallel Execution] Y --> Z[Result Correlation] Z --> AA[Pattern Recognition] AA --> AB[Intelligence Report] end subgraph "Security Layer" AC[Rate Limiting] --> AD[Proxy Rotation] AD --> AE[User-Agent Spoofing] AE --> AF[Session Management] end ``` 🔬 数字模拟器 系统架构组件 ``` # Core Architecture Framework class GoblinTsunamiArchitecture: """Digital simulator for the Goblin Tsunami OSINT framework""" def __init__(self): self.modules = { 'dork_engine': DorkGenerationEngine(), 'platform_modules': { 'instagram': InstagramModule(), 'threads': ThreadsModule(), 'facebook': FacebookModule(), 'tiktok': TikTokModule() }, 'analytics': AnalyticsEngine(), 'security': SecurityLayer(), 'output': OutputFormatter() } def execute_operation(self, target: str) -> IntelligenceReport: """ Complete digital footprint analysis pipeline Flow: 1. Input Validation → 2. Dork Generation → 3. Platform Query 4. Data Extraction → 5. Pattern Analysis → 6. Report Generation """ pass class DorkGenerationEngine: """Intelligent dork generation system""" TRACKING_CATEGORIES = { 'location': 45, # Location-based queries 'social': 38, # Social network analysis 'professional': 32, # Career & education 'personal': 41, # Personal information 'content': 35, # Media & posts 'engagement': 28, # Interaction patterns 'temporal': 24, # Time-based analysis 'behavioral': 33, # Behavior patterns 'network': 29, # Connection mapping 'digital': 36 # Digital footprint } def generate_dorks(self, username: str, platform: str) -> List[str]: """Generate comprehensive search queries""" pass class IntelligenceReport: """Digital forensic report structure""" def __init__(self, target: str): self.metadata = { 'timestamp': datetime.now(), 'target': target, 'total_dorks': 0, 'platforms_analyzed': [], 'categories_covered': [] } self.findings = { 'location_history': [], 'social_connections': [], 'professional_info': [], 'activity_patterns': [], 'digital_footprint': [] } ``` 📊 数据流程图 ``` sequenceDiagram participant User participant CLI as Command Line Interface participant DGE as Dork Generation Engine participant PM as Platform Modules participant S as Security Layer participant O as Output Handler User->>CLI: Enter Target Username CLI->>DGE: Generate Queries DGE->>PM: Distribute Platform Queries par Instagram Threads PM->>S: Apply Security Measures S-->>PM: Proxy & Headers Applied PM->>PM: Execute Searches and Facebook TikTok PM->>S: Apply Security Measures S-->>PM: Proxy & Headers Applied PM->>PM: Execute Searches end PM->>O: Aggregate Results O->>User: Display Intelligence Report alt Save Requested User->>O: Save Files O->>User: Confirmation & File Paths end ``` 🛡️ 数字模拟器可视化 ``` import matplotlib.pyplot as plt import numpy as np from dataclasses import dataclass from typing import Dict, List import seaborn as sns @dataclass class SimulationMetrics: """Simulated performance metrics for the architecture""" platform: str dorks_generated: int success_rate: float avg_response_time: float categories_covered: int class DigitalSimulator: """Interactive digital architecture simulator""" def __init__(self): self.platforms = ['Instagram', 'Threads', 'Facebook', 'TikTok'] self.metrics = self.generate_metrics() self.visualize_architecture() def generate_metrics(self) -> Dict[str, SimulationMetrics]: """Generate realistic simulation metrics""" return { 'Instagram': SimulationMetrics( platform='Instagram', dorks_generated=246, success_rate=0.87, avg_response_time=1.2, categories_covered=23 ), 'Threads': SimulationMetrics( platform='Threads', dorks_generated=128, success_rate=0.82, avg_response_time=0.9, categories_covered=18 ), 'Facebook': SimulationMetrics( platform='Facebook', dorks_generated=192, success_rate=0.79, avg_response_time=1.5, categories_covered=21 ), 'TikTok': SimulationMetrics( platform='TikTok', dorks_generated=173, success_rate=0.84, avg_response_time=1.1, categories_covered=19 ) } def visualize_architecture(self): """Create visual representation of system architecture""" fig, axes = plt.subplots(2, 2, figsize=(16, 12)) fig.suptitle('GOBLIN TSUNAMI - Digital Architecture Simulator', fontsize=16) # Plot 1: Dork Distribution ax1 = axes[0, 0] platforms = [m.platform for m in self.metrics.values()] dorks = [m.dorks_generated for m in self.metrics.values()] colors = ['#00ff00', '#00ffff', '#ff00ff', '#ffff00'] ax1.bar(platforms, dorks, color=colors, alpha=0.7, edgecolor='white') ax1.set_title('Dork Generation Distribution') ax1.set_ylabel('Number of Dorks') ax1.grid(True, alpha=0.3) # Plot 2: Success Rate ax2 = axes[0, 1] success = [m.success_rate * 100 for m in self.metrics.values()] ax2.barh(platforms, success, color='#00ff88', alpha=0.7) ax2.set_title('Query Success Rate') ax2.set_xlabel('Success Rate (%)') ax2.grid(True, alpha=0.3) # Plot 3: Category Coverage ax3 = axes[1, 0] categories = [m.categories_covered for m in self.metrics.values()] ax3.pie(categories, labels=platforms, autopct='%1.1f%%', colors=['#00ff00', '#00ffff', '#ff00ff', '#ffff00']) ax3.set_title('Category Coverage Distribution') # Plot 4: Performance Matrix ax4 = axes[1, 1] data = np.array([[m.dorks_generated, m.success_rate * 100, m.avg_response_time, m.categories_covered] for m in self.metrics.values()]) im = ax4.imshow(data, cmap='viridis', aspect='auto') ax4.set_xticks(range(4)) ax4.set_xticklabels(['Dorks', 'Success %', 'Response(s)', 'Categories']) ax4.set_yticks(range(4)) ax4.set_yticklabels(platforms) plt.colorbar(im, ax=ax4) ax4.set_title('Performance Matrix') plt.tight_layout() plt.show() def run_simulation(self): """Execute full digital simulation""" print("🔬 GOBLIN TSUNAMI - Digital Architecture Simulator") print("=" * 60) for platform, metrics in self.metrics.items(): print(f"\n📱 Platform: {platform}") print(f" ├─ Dorks Generated: {metrics.dorks_generated}") print(f" ├─ Success Rate: {metrics.success_rate * 100:.1f}%") print(f" ├─ Avg Response: {metrics.avg_response_time}s") print(f" └─ Categories: {metrics.categories_covered}") print("\n" + "=" * 60) print("✅ Simulation Complete - Architecture Verified") ``` 🚀 安装说明 前置条件 ``` Python 3.8+ pip3 git ``` 快速安装 ``` # Clone repository git clone https://github.com/sylhetyhackvenger/GOBLIN-Tsunami cd GOBLIN-Tsunami # Install dependencies pip install -r requirements.txt # Run the tool python3 goblin_tsunami.py ``` 环境要求 ``` python>=3.8 typing>=3.7.4 datetime>=4.3 threading>=0.1 warnings>=0.1 ``` 📖 使用指南 基本用法 ``` python3 goblin_tsunami.py ``` 交互式工作流 1. 输入目标用户名：输入要调查的社交媒体账号 2. Dork 生成：工具在所有平台上生成 400+ 条查询 3. 查看结果：通过分页浏览分类的 dork 4. 保存情报：将完整的报告导出为文本文件 导航控制 ``` [N]ext - View next page of dorks [P]revious - View previous page [Q]uit - Exit current platform view ``` 输出文件 ``` dorks_instagram_username_20260103_120000.txt dorks_threads_username_20260103_120000.txt dorks_facebook_username_20260103_120000.txt dorks_tiktok_username_20260103_120000.txt dorks_all_username_20260103_120000.txt # Complete bundle ``` 🎯 应用场景 专业应用 · 网络安全审计：评估员工的数字暴露情况 · 威胁情报：监控威胁行为者的公开足迹 · 渗透测试：社会工程学攻击面映射 · 企业调查：尽职调查与背景审查 · 数字取证：为调查收集证据 道德考量 ✅ 应该： · 在调查个人时获得明确同意 · 仅用于合法的安全目的 · 遵守所有适用的法律和法规 · 尊重隐私设置和界限 ❌ 切勿： · 用于骚扰或跟踪 · 针对未成年人或弱势群体 · 绕过安全措施或隐私控制 · 未经适当授权分享调查结果 📊 统计概览 ``` GENERATION_STATISTICS = { 'total_dorks': 739, 'instagram': 246, 'threads': 128, 'facebook': 192, 'tiktok': 173, 'tracking_categories': 25, 'platform_coverage': 4, 'avg_dorks_per_platform': 184.75, 'file_output_formats': ['txt'], 'security_measures': ['rate_limiting', 'proxy_support', 'user_agent_rotation'] } ``` 🔐 安全与合规 内置安全功能 · 速率限制：防止滥用和被检测 · 代理支持：可选的代理轮换 · User-Agent 伪装：避免模式检测 · 道德准则：内置合规提醒 法律免责声明 ``` This tool is provided for educational and professional security testing purposes only. Users are solely responsible for ensuring compliance with all applicable laws, regulations, and platform terms of service. Unauthorized use of this tool may violate privacy laws and computer fraud statutes. ``` 📝 许可证 该项目基于 MIT 许可证授权 - 有关详细信息，请参阅 LICENSE 文件。 🙏 致谢 · OSINT 社区：感谢在该领域的不断创新 · Google Dorking 先驱：感谢其提供的基础技术 · 开源贡献者：感谢维护该生态系统 📞 联系与支持 GitHub Issues：报告 Bug / 申请新功能 文档：完整文档 安全报告：请通过电子邮件私下报告安全问题

标签：Python, 威胁情报, 开发者工具, 数字足迹分析, 文档结构分析, 无后门, 社交媒体分析, 逆向工具