MariuszBsk/-Advanced-Web-Vulnerability-Scanner

🛡️ 高级 Web 漏洞扫描器 一款智能的 AI 驱动 Web 安全扫描器，结合了深度学习检测与自动化 payload 测试，用于识别 SQL 注入和 XSS 漏洞。 🎯 功能特性 ``` 🤖 Dual AI Model Architecture Detection Model: Identifies vulnerabilities in HTML/JavaScript content Payload Model: Tests and ranks payload effectiveness 🔍 Smart Detection Methods TensorFlow/Keras deep learning models for pattern recognition Fallback regex-based pattern matching Context-aware vulnerability identification 🎯 Supported Vulnerabilities SQL Injection (SQLi) Cross-Site Scripting (XSS) Custom vulnerability types support 📊 Comprehensive Reporting Markdown reports with actionable attack scenarios Severity scoring (Critical → Info) Exact location mapping (line numbers, HTML elements) Copy-paste ready payloads Remediation recommendations ⚡ Smart Features Configurable request delays for responsible scanning Session management with realistic headers Automatic payload effectiveness ranking HTML/JavaScript parser with precise location tracking 🚀 Quick Start ``` 前置条件 bash pip install requests beautifulsoup4 numpy tensorflow keras 模型设置 请将你训练好的模型放置在以下目录结构中： scanner/ ├── models2/ │ ├── vulnerability_model.h5 # 检测模型 │ ├── tokenizer.pkl # 检测 tokenizer │ └── label_mapping.pkl # 漏洞标签 └── models/ ├── payload_model.h5 # Payload 有效性模型 └── payload_tokenizer.pkl # Payload tokenizer ``` 💡 Example Output 🚀 Starting comprehensive vulnerability assessment... ``` ✅ 使用新的检测模型发现了 3 个漏洞 🎉 扫描成功完成！ 📊 发现漏洞数量：3 🔴 严重：1 🟠 高危：2 📄 已生成报告：vulnerability_scan_report_20241201-143022.md 基本用法 # 使用模式匹配进行快速扫描 python vulnerability_scanner.py https://target-site.com # 结合 payload 测试的完整 AI 扫描 python vulnerability_scanner.py https://target-site.com -a # 带有延迟的负责任扫描 python vulnerability_scanner.py https://target-site.com -d 0.5 -a 📋 生成的报告包含 ``` Executive Summary: Severity breakdown and statistics Detailed Findings: Exact location, code context, HTML elements Attack Scenarios: Step-by-step exploitation guidance Tested Payloads: Ranked by AI-predicted effectiveness Remediation Steps: OWASP-compliant fixes User Input → HTML Parser → Element Extraction → Detection Model ↓ Vulnerability Found? ↓ Payload Testing Model ↓ Comprehensive Report 🔧 Command Line Arguments ``` 参数 描述 默认值 url 目标网站 URL（自动添加 http://） 必填 -d, --delay 请求之间的秒数 0 -a, --auto 运行自动化 payload 测试 False ⚠️ 法律声明 本工具仅供授权的安全测试使用。用户必须： ``` Obtain explicit permission before scanning any website Comply with all applicable laws and regulations Accept full responsibility for any damages or legal consequences ``` 🛠️ 技术细节 ``` Framework: TensorFlow/Keras for deep learning models Parsing: BeautifulSoup4 for HTML/JavaScript extraction Preprocessing: Sequence padding and tokenization Confidence Threshold: 60% for detection accuracy Payload Ranking: AI-model predicted effectiveness scores ``` 📈 未来增强计划 ``` Multi-threaded scanning Custom payload injection engine CSRF and SSRF detection API endpoint fuzzing Interactive CLI mode Docker containerization ``` 🤝 参与贡献 欢迎贡献代码！改进方向包括： ``` Additional vulnerability types (Command Injection, Path Traversal) Model training scripts and datasets Burp Suite/ZAP integration CI/CD pipeline integration ``` 📚 参考资料 ``` OWASP Testing Guide PortSwigger Research CWE Top 25 ``` ⚠️ 重要提示：此扫描器需要预训练的 TensorFlow 模型。训练脚本和数据集可应要求提供。

标签：AI安全, AMSI绕过, Apex, Chat Copilot, CISA项目, DNS枚举, Keras, Payload测试, Python, SQL注入检测, TensorFlow, Web安全, XSS漏洞扫描, 人工智能, 加密, 反取证, 威胁检测, 安全报告, 安全评估, 无后门, 机器学习, 深度学习, 漏洞扫描器, 用户模式Hook绕过, 网络安全工具, 自动化渗透测试, 蓝队分析, 载荷生成, 逆向工具