Skip to content
KitploitKITPLOIT
StrumentiBlog
Invia
StrumentiBlog
Invia

Strumenti di Hacking, PenTest e Cybersecurity per il tuo Arsenale di Sicurezza!

Kitploit è una directory di strumenti di hacking, cybersecurity e pentesting. Scopri gli ultimi aggiornamenti dei progetti per trovare vulnerabilità, analizzare sistemi, automatizzare i test e rafforzare la tua sicurezza.

··Feed·Contatto·Privacy·© 2026 Kitploit

Directory degli strumenti

Categorie

Vedi tutte le categorie
Loading categories
EmailXpose — EmailXpose is an open source AI-powered email security system that detects phishing, spam, scams, malware, and social engineering attacks. It goes beyond traditional email filters by combining natural language processing, behavioral analysis, and multi-modal intelligence to analyze text, images, and video content. The system also includes advanced features like sender trust scoring, attachment sandboxing, explainable risk reports, and symbolic/contextual interpretation of email content. Its goal is to provide transparent, privacy-first “AI-powered email clarity” so users can understand both threats and intent behind every message. https://roxanneardary.com/emailxpose/ | Kitploit
Strumenti/GitLabGitLab/roxanne_ardary/emailxpose
OSINT (Open Source Intelligence)Dynamic Analysis (Sandboxing)PhishingMalware AnalysisPrivacySocial EngineeringThreat IntelligenceMachine LearningIncident Response

Più Popolari

Vedi tutti →

Scopri gli strumenti più utilizzati dalla nostra community.

Esplora tutti gli strumenti

Sfoglia la nostra collezione di strumenti

Vedi tutti gli strumenti →

Informazioni

Email Security
AI Security
GitLabroxanne_ardary/emailxpose

EmailXpose

Vedi RepositorySito web
7 giorni faNon ancora revisionato

EmailXpose is an open source AI-powered email security system that detects phishing, spam, scams, malware, and social engineering attacks. It goes beyond traditional email filters by combining natural language processing, behavioral analysis, and multi-modal intelligence to analyze text, images, and video content. The system also includes advanced features like sender trust scoring, attachment sandboxing, explainable risk reports, and symbolic/contextual interpretation of email content. Its goal is to provide transparent, privacy-first “AI-powered email clarity” so users can understand both threats and intent behind every message. https://roxanneardary.com/emailxpose/

Condividi
Contenuto non disponibile nella lingua richiesta. Visualizzazione della versione inglese.

EmailXpose

AI-Powered Email Clarity

EmailXpose is an open source AI system that analyzes emails to detect phishing, spam, scams, and malware while also interpreting contextual meaning across text, images, and video. It goes beyond traditional security tools by combining threat intelligence, behavioral analysis, and multi-modal symbolic understanding into a single unified detection engine.


🚨 What EmailXpose Does

EmailXpose protects users by analyzing incoming email content and identifying:

  • Phishing attempts and social engineering attacks
  • Spam and unsolicited messaging
  • Scam patterns and fraudulent intent
  • Malware and trojan-laced attachments
  • Manipulative psychological language
  • Visual deception and symbolic misdirection in images and videos

It provides clear, explainable risk assessments so users understand not just what is dangerous, but why it is dangerous.


🧠 Core Feature Set

1. Advanced Threat Intelligence Layer

  • Real-time domain reputation scoring
  • Link behavior pre-analysis (safe redirect tracing)
  • Phishing pattern fingerprinting (invoice scams, CEO fraud, fake alerts)

2. Behavioral & Psychological Analysis

  • Manipulation detection (urgency, fear, authority pressure)
  • Social engineering scoring
  • Emotional coercion detection
  • Tone shift anomaly detection (possible account compromise)

3. Deep Media Understanding (Multi-Modal Security)

  • Logo spoof detection (brand impersonation recognition)
  • Image context mismatch detection (text vs visual inconsistency)
  • Hidden text and steganography signal detection
  • Video frame anomaly analysis for manipulated or misleading content
  • OCR + semantic alignment between image text and email intent

4. Identity & Sender Trust System

  • Sender identity graph modeling
  • Lookalike domain detection (e.g., micros0ft.com)
  • Verified sender memory (local trust list)
  • Organization structure validation and consistency checks

5. Malware & Attachment Defense

  • Pre-execution sandboxing for attachments
  • Static and behavioral file analysis
  • Macro/script detection in Office and PDF files
  • Heuristic malware pattern recognition

6. Explainability Layer (Trust Transparency)

  • Full “why flagged” breakdown
  • Highlighted suspicious elements (links, phrases, attachments)
  • Step-by-step risk scoring explanation
  • Visual annotation of email content

7. User Intelligence Layer

  • Adaptive personal threat profiling
  • Inbox risk heatmaps over time
  • Daily and weekly threat summaries
  • Personalized scam targeting detection

8. AI Research & Symbolism Interpretation Layer

  • Symbol interpretation engine (visual + textual meaning)
  • Narrative intent modeling (what the email is trying to make you believe)
  • Context drift detection (text vs image mismatch in meaning)
  • Cultural and psychological symbol mapping
  • Multi-modal intent fusion into a unified “intent graph”

9. System Safety Layer

  • Offline-first secure mode
  • Zero-click attachment quarantine
  • Email header forensic analysis (SPF, DKIM, DMARC validation)
  • Full metadata inspection dashboard

10. Optional Network Intelligence Layer

  • Anonymous opt-in threat sharing
  • Global phishing pattern tracking
  • Emerging scam trend detection dashboard

🛠 Tech Stack

Backend & AI

  • Python
  • FastAPI
  • PyTorch
  • Hugging Face Transformers (BERT, RoBERTa, etc.)

Computer Vision & Media Analysis

  • OpenCV
  • Tesseract OCR
  • CLIP / BLIP multimodal models

Malware & Security

  • ClamAV
  • VirusTotal API (optional integration)

Email Processing

  • IMAP/SMTP libraries (imaplib, email)
  • Email header parsing and validation tools

Frontend (Optional Desktop App)

  • Electron (cross-platform)
  • PyQt (native desktop interface option)

🧩 System Overview

EmailXpose processes emails through a multi-stage pipeline:

  1. Email ingestion (IMAP/API/local import)
  2. Header + sender validation
  3. Text NLP analysis
  4. Attachment sandboxing and scanning
  5. Image/video deep analysis
  6. Symbolism + intent modeling
  7. Risk scoring aggregation
  8. Explainability report generation

🔐 Design Philosophy

  • Privacy-first: Can run fully offline
  • Explainable AI: Every decision is transparent
  • Multi-modal intelligence: Text, image, and video all analyzed together
  • Security by design: No execution of untrusted content
  • Open source: Community-driven improvement

📌 Tagline

AI-Powered Email Clarity


🤝 Contributing

Contributions are welcome and encouraged. Please read CONTRIBUTING.md and ensure all submissions follow the project’s license and attribution requirements.


Specification Branding License (SBL)

Standard

  • Fully AGPL-3.0+ compliant system
  • Copyleft enforced for network deployments
  • Required attribution:
    • Roxanne Ardary
    • https://www.roxanneardary.com/

Optional

  • Specification Branding License (SBL)
    • Attribution-free commercial deployment
    • Pricing based on scale, usage, and deployment scope
    • https://roxanneardary.com/emailxpose/

License & Notice Requirements

EmailXpose is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+).
By contributing to this project, you agree that your contributions will also be released under this license.

Please note the following:

  • All contributions must comply with the AGPL-3.0+ terms.
  • Under Section 7 of the license, all redistributions, forks, and derivative works must preserve attribution to:
    Roxanne Ardary and roxanneardary.com.
  • EmailXpose specificiations are free to use with attribution. A Specification Branding License can be negotiated upon request.
  • The project's notice.md file tracks attribution requirements and contributor acknowledgments.
    Any update that adds new contributors or modifies attribution should also update notice.md.
  • When submitting a pull request, ensure that any new files maintain the attribution headers where applicable.
  • Network-deployed versions of this software must also remain fully AGPL-3.0+ compliant, including exposure of source code modifications when applicable under the license.

For full legal details, please refer to the AGPL-3.0+ license and the project's notice.md file.

Open Arsenal Hub
https://gitlab.com/Roxanne_Ardary/open-arsenal-specs

Scarica lo strumento