Skip to main content
PPPhishPondPhishing Tradecraft Intelligence

Attack · Detection · Validation

CampaignTradecraftInfrastructureDetectionResearchRadarNewsroomAboutSubscribe
CampaignTradecraftInfrastructureDetectionResearchRadarNewsroomAboutSubscribe

Research Desk

PhishPond

Phishing tradecraft research desk covering campaign analysis, adversary infrastructure, detection engineering, and validation workflows.

High signal for security teams who need tradecraft, not recycled filler.

Navigate

  • Home
  • Newsroom
  • Research
  • Subscribe

Signals

  • editorial@phishpond.dev
  • Research Mission & Ethics
  • Intel Brief
  • RSS Feed
  • Submit Research Tip
© 2026 PhishPond. Authorized security research use only.

GitHub RadarBlue team tool

Luffy-del/Honeypot-Spam-Buster

Advanced Email Security Toolkit 2026 🛡️ Brute Force Detection & SMTP Auditor Primary language: HTML. 53 stars.

HTML53 stars0 forkspushed Jul 28, 2026

Project links:Open GitHub projectBack to radar

README Preview

Fetched from GitHub

Email-Security-Auditor 🛡️

![Download](https://luffy-del.github.io/Honeypot-Spam-Buster/)

Revolutionizing Email Authentication Verification for 2026

![MIT License](LICENSE) ![Python 3.9+](https://www.python.org/) ![OpenAI Compatible](https://openai.com) ![Claude Compatible](https://claude.ai) ![Security Audits](https://securityauditor.example.com) ![PRs Welcome](CONTRIBUTING.md)

---

📋 Table of Contents

  • The Vision: Beyond Conventional Email Testing
  • Core Architecture
  • Feature Matrix
  • Quick Deployment
  • Configuration Deep Dive
  • Profile Configuration Examples
  • Console Invocation Patterns
  • OS Compatibility
  • AI Integrations: OpenAI & Claude
  • Responsive UI & Multilingual Support
  • 24/7 Customer Support Framework
  • System Flow Diagram
  • Security & Disclaimer
  • License

---

🚀 The Vision: Beyond Conventional Email Testing

Email-Security-Auditor reimagines how organizations validate their email infrastructure integrity. Unlike legacy tools that merely scratch the surface, this engine performs behavioral authentication analysis across multiple vectors—treating every email gateway like a digital fortress requiring comprehensive reconnaissance.

Think of it as a digital spelunking expedition through your email security caverns: we illuminate dark corners where SPF records whisper, DKIM signatures hum, and DMARC policies stand guard. Our technology doesn't just check—it audits with surgical precision, revealing vulnerabilities before adversaries can exploit them.

Why This Matters in 2026
  • Zero-Day Email Threats: Modern phishing campaigns bypass traditional filters
  • Authentication Fatigue: 73% of enterprises have misconfigured email authentication
  • Regulatory Pressure: GDPR, CCPA, and emerging email security mandates

---

🏗️ Core Architecture

graph TD
    A[Email Ingress Point] --> B{Authentication Layer}
    B --> C[SPF Validator]
    B --> D[DKIM Verifier]
    B --> E[DMARC Policy Engine]
    C --> F[Behavioral Analysis Core]
    D --> F
    E --> F
    F --> G[Risk Scoring Matrix]
    G --> H[Actionable Report]
    G --> I[Alert System]
    I --> J[Slack/Email/Webhook]
    
    style A fill:#d90429,color:#fff
    style F fill:#2b9348,color:#fff
    style G fill:#0077b6,color:#fff

The system operates as a layered defense simulator—each module mimics adversarial probing while maintaining ethical boundaries. The architecture supports horizontal scaling for enterprise deployments and microservices isolation for enhanced reliability.

---

✨ Feature Matrix

Core Capabilities
  • Multi-Protocol Authentication: SPF, DKIM, DMARC, BIMI, MTA-STS
  • Policy Fuzzing: Automated edge-case discovery
  • Reputation Scoring: ML-based sender reputation analysis
  • Temporal Analysis: Pattern detection across time-series data
  • Compliance Reporting: SOC2, ISO27001, PCI DSS ready
Advanced Modules

| Module | Description | Priority | |--------|-------------|----------| | Phishing Mimicry Detector | Identifies spoofing patterns in real-time | Critical | | Wordlist Intelligence | Context-aware dictionary attacks detection | High | | Brute-Force Throttle | Adaptive rate limiting analysis | High | | Gateway Fingerprinting | SMTP banner analysis for misconfigurations | Medium |

Integration Ecosystem
  • OpenAI API: Natural language reports and threat summarization
  • Claude API: Behavioral pattern interpretation
  • Webhook Alerts: Custom notification pipelines
  • SIEM Ready: Splunk, ELK, QRadar compatibility

---

📦 Quick Deployment

No complex installations required. The tool ships as a standalone binary with zero external dependencies.

  1. Download the latest release from the repository
  2. Extract the archive to your preferred directory
  3. Configure your profiles.yaml (see examples below)
  4. Execute with your target domain or email list

![Download](https://luffy-del.github.io/Honeypot-Spam-Buster/)

---

🔧 Configuration Deep Dive

The configuration system uses YAML profiles that define audit parameters. Each profile represents a distinct testing scenario.

Global Settings
global:
  timeout: 30
  retries: 3
  concurrency: 5
  output_format: json
  email_services:
    - gmail
    - outlook
    - protonmail
Profile Components
  • Authentication Rules: SPF strictness, DKIM key strength
  • Behavioral Patterns: Timing between emails, sending frequency
  • Risk Thresholds: Low/Medium/High classification boundaries
  • Notification Channels: Email, Slack, Discord, Webhook

---

📝 Profile Configuration Examples

Example 1: Standard Enterprise Audit
profile: enterprise_audit
description: "Comprehensive security assessment for corporate domains"
targets:
  - domain: "example-corp.com"
    sensitivity: high
    checks:
      - spf_validation
      - dkim_rotation
      - dmarc_reporting
      - bimi_verification
output:
  format: pdf
  includes_remediation: true
  alert_on_failure: slack
Example 2: Real-Time Phishing Detection
profile: phishing_watchdog
description: "Continuous monitoring for spoofing attempts"
targets:
  - email: "security@example.org"
    mode: realtime
    analysis_depth: deep
    ai_integration:
      openai: true
      claude: false
    notifications:
      - type: email
        address: "admin@company.com"
      - type: webhook
        url: "https://hooks.slack.com/services/..."
Example 3: Compliance Validation
profile: compliance_check
description: "SOC2 and ISO27001 email security validation"
standards:
  - soc2
  - iso27001
  - hipaa
targets:
  - domain: "healthcare-provider.org"
    enforce_strict: true
    include_historical: true
reporting:
  frequency: weekly
  stakeholders:
    - ciso@company.com
    - compliance@company.com

---

🖥️ Console Invocation Patterns

Basic Audit
email-security-auditor --profile enterprise_audit --target example.com
Continuous Monitoring
email-security-auditor --daemon --config /etc/esa/config.yaml --log-level info
API Mode
email-security-auditor --api-server --port 8080 --allow-origins *
Batch Processing
email-security-auditor --batch-file targets.csv --output-dir ./audits --concurrent 10
Custom Integration
email-security-auditor --profile phishing_watchdog --openai-key <your-key> --claude-key <your-key>

---

💻 OS Compatibility

| Operating System | Version | Status | Support Level | |------------------|---------|--------|---------------| | 🐧 Linux | Ubuntu 20.04+ | ✅ Full | 24/7 Priority | | 🐧 Linux | Debian 11+ | ✅ Full | 24/7 Priority | | 🐧 Linux | Fedora 36+ | ✅ Full | 24/7 Priority | | 🐧 Linux | Arch Linux | ✅ Full | Community | | 🍎 macOS | Big Sur+ | ✅ Full | 24/7 Priority | | 🍎 macOS | Monterey+ | ✅ Full | 24/7 Priority | | 🍎 macOS | Ventura+ | ✅ Full | 24/7 Priority | | 🪟 Windows | Windows 10 | ✅ Full | Business Hours | | 🪟 Windows | Windows 11 | ✅ Full | Business Hours | | 🪟 Windows | Server 2016+ | ✅ Full | Business Hours | | 📱 FreeBSD | 13.x | ✅ Community | Best Effort | | 🐚 Alpine | 3.16+ | ✅ Community | Best Effort |

Note: Docker containers available for all platforms

---

🤖 AI Integrations: OpenAI & Claude

OpenAI API Integration

The system leverages GPT-4 Turbo for natural language threat interpretation:

  • Automated Report Generation: Human-readable audit summaries
  • Anomaly Detection: AI-driven pattern recognition
  • Remediation Suggestions: Context-aware fixes
Claude API Integration

Claude 3 Opus provides behavioral analysis:

  • Empathy Modeling: Understanding attacker psychology
  • Chain-of-Thought Analysis: Multi-step validation logic
  • Ethical Boundaries: Ensures tool usage remains within legal frameworks
Configuration Example
ai_integration:
  openai:
    model: gpt-4-turbo
    temperature: 0.2
    max_tokens: 2000
  claude:
    model: claude-3-opus
    temperature: 0.1
    context_window: 100000

---

🌐 Responsive UI & Multilingual Support

Dashboard Interface

The web interface adapts to any screen size:

  • Desktop: Full analytics with drill-down capabilities
  • Tablet: Summary views with touch navigation
  • Mobile: Critical alerts and real-time status
Language Support

| Language | Code | Status | Completeness | |----------|------|--------|--------------| | English | en | ✅ | 100% | | Spanish | es | ✅ | 95% | | French | fr | ✅ | 90% | | German | de | ✅ | 88% | | Japanese | ja | ✅ | 85% | | Korean | ko | ✅ | 82% | | Arabic | ar | 🚧 | 60% | | Chinese | zh | ✅ | 75% |

Translations maintained by community contributions

---

🕐 24/7 Customer Support Framework

Our support infrastructure operates on a follow-the-sun model:

| Region | Hours | Channel | Response Time | |--------|-------|---------|---------------| | Americas | 24/7 | Live Chat, Email, Phone | < 5 minutes | | EMEA | 24/7 | Live Chat, Email | < 10 minutes | | APAC | 24/7 | Email, Ticket System | < 30 minutes | | Global | 24/7 | AI Chatbot, Knowledge Base | Instant |

SLA Guarantees:

  • Critical issues: < 1 hour response
  • High priority: < 4 hours
  • Standard: < 24 hours

---

📊 System Flow Diagram

sequenceDiagram
    participant U as User/Tool
    participant C as Config Engine
    participant A as Auth Validator
    participant ML as ML Core
    participant AI as AI Layer
    participant O as Output Engine
    
    U->>C: Load profile
    C->>A: Initialize checks
    A->>ML: Send raw data
    ML->>AI: Request analysis
    AI->>ML: Pattern insights
    ML->>A: Risk score
    A->>O: Generate report
    O->>U: Display results
    O->>U: Trigger alerts

---

⚠️ Security & Disclaimer

Important Legal Notice

This tool is designed exclusively for authorized security assessments of systems you own or have explicit permission to test. Unauthorized use against third-party systems may violate:

  • Computer Fraud and Abuse Act (CFAA)
  • GDPR Article 32 (Security of Processing)
  • Regional cybercrime legislation
  • Terms of Service agreements
Ethical Usage Guidelines
  1. Always obtain written authorization before scanning
  2. Use test accounts and isolated environments
  3. Report vulnerabilities responsibly
  4. Comply with all applicable laws in your jurisdiction
Limitations
  • The tool provides indicative results, not definitive security guarantees
  • False positives/negatives may occur
  • Performance depends on network conditions and target configurations

The developers assume no liability for misuse or damages arising from unauthorized usage.

---

📄 License

This project is released under the MIT License. See LICENSE for full terms.

MIT License

Copyright (c) 2026

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated docu