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Before yesterdayKitPloit - PenTest Tools!

Bytesrevealer - Online Reverse Enginerring Viewer

By: Unknown


Bytes Revealer is a powerful reverse engineering and binary analysis tool designed for security researchers, forensic analysts, and developers. With features like hex view, visual representation, string extraction, entropy calculation, and file signature detection, it helps users uncover hidden data inside files. Whether you are analyzing malware, debugging binaries, or investigating unknown file formats, Bytes Revealer makes it easy to explore, search, and extract valuable information from any binary file.

Bytes Revealer do NOT store any file or data. All analysis is performed in your browser.

Current Limitation: Files less than 50MB can perform all analysis, files bigger up to 1.5GB will only do Visual View and Hex View analysis.


Features

File Analysis

  • Chunked file processing for memory efficiency
  • Real-time progress tracking
  • File signature detection
  • Hash calculations (MD5, SHA-1, SHA-256)
  • Entropy and Bytes Frequency analysis

Multiple Views

File View

  • Basic file information and metadata
  • File signatures detection
  • Hash values
  • Entropy calculation
  • Statistical analysis

Visual View

  • Binary data visualization
  • ASCII or Bytes searching
  • Data distribution view
  • Highlighted pattern matching

Hex View

  • Traditional hex editor interface
  • Byte-level inspection
  • Highlighted pattern matching
  • ASCII representation
  • ASCII or Bytes searching

String Analysis

  • ASCII and UTF-8 string extraction
  • String length analysis
  • String type categorization
  • Advanced filtering and sorting
  • String pattern recognition
  • Export capabilities

Search Capabilities

  • Hex pattern search
  • ASCII/UTF-8 string search
  • Regular expression support
  • Highlighted search results

Technical Details

Built With

  • Vue.js 3
  • Tailwind CSS
  • Web Workers for performance
  • Modern JavaScript APIs

Performance Features

  • Chunked file processing
  • Web Worker implementation
  • Memory optimization
  • Cancelable operations
  • Progress tracking

Getting Started

Prerequisites

# Node.js 14+ is required
node -v

Docker Usage

docker-compose build --no-cache

docker-compose up -d

Now open your browser: http://localhost:8080/

To stop the docker container

docker-compose down

Installation

# Clone the repository
git clone https://github.com/vulnex/bytesrevealer

# Navigate to project directory
cd bytesrevealer

# Install dependencies
npm install

# Start development server
npm run dev

Building for Production

# Build the application
npm run build

# Preview production build
npm run preview

Usage

  1. File Upload
  2. Click "Choose File" or drag and drop a file
  3. Progress bar shows upload and analysis status

  4. Analysis Views

  5. Switch between views using the tab interface
  6. Each view provides different analysis perspectives
  7. Real-time updates as you navigate

  8. Search Functions

  9. Use the search bar for pattern matching
  10. Toggle between hex and string search modes
  11. Results are highlighted in the current view

  12. String Analysis

  13. View extracted strings with type and length
  14. Filter strings by type or content
  15. Sort by various criteria
  16. Export results in multiple formats

Performance Considerations

  • Large files are processed in chunks
  • Web Workers handle intensive operations
  • Memory usage is optimized
  • Operations can be canceled if needed

Browser Compatibility

  • Chrome 80+
  • Firefox 75+
  • Safari 13.1+
  • Edge 80+

Contributing

  1. Fork the project
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Security Considerations

  • All strings are properly escaped
  • Input validation is implemented
  • Memory limits are enforced
  • File size restrictions are in place

Future Enhancements

  • Additional file format support
  • More visualization options
  • Pattern recognition improvements
  • Advanced string analysis features
  • Export/import capabilities
  • Collaboration features


PANO - Advanced OSINT Investigation Platform Combining Graph Visualization, Timeline Analysis, And AI Assistance To Uncover Hidden Connections In Data

By: Unknown


PANO is a powerful OSINT investigation platform that combines graph visualization, timeline analysis, and AI-powered tools to help you uncover hidden connections and patterns in your data.

Getting Started

  1. Clone the repository: bash git clone https://github.com/ALW1EZ/PANO.git cd PANO

  2. Run the application:

  3. Linux: ./start_pano.sh
  4. Windows: start_pano.bat

The startup script will automatically: - Check for updates - Set up the Python environment - Install dependencies - Launch PANO

In order to use Email Lookup transform You need to login with GHunt first. After starting the pano via starter scripts;

  1. Select venv manually
  2. Linux: source venv/bin/activate
  3. Windows: call venv\Scripts\activate
  4. See how to login here

πŸ’‘ Quick Start Guide

  1. Create Investigation: Start a new investigation or load an existing one
  2. Add Entities: Drag entities from the sidebar onto the graph
  3. Discover Connections: Use transforms to automatically find relationships
  4. Analyze: Use timeline and map views to understand patterns
  5. Save: Export your investigation for later use

πŸ” Features

πŸ•ΈοΈ Core Functionality

  • Interactive Graph Visualization
  • Drag-and-drop entity creation
  • Multiple layout algorithms (Circular, Hierarchical, Radial, Force-Directed)
  • Dynamic relationship mapping
  • Visual node and edge styling

  • Timeline Analysis

  • Chronological event visualization
  • Interactive timeline navigation
  • Event filtering and grouping
  • Temporal relationship analysis

  • Map Integration

  • Geographic data visualization
  • Location-based analysis
  • Interactive mapping features
  • Coordinate plotting and tracking

🎯 Entity Management

  • Supported Entity Types
  • πŸ“§ Email addresses
  • πŸ‘€ Usernames
  • 🌐 Websites
  • πŸ–ΌοΈ Images
  • πŸ“ Locations
  • ⏰ Events
  • πŸ“ Text content
  • πŸ”§ Custom entity types

πŸ”„ Transform System

  • Email Analysis
  • Google account investigation
  • Calendar event extraction
  • Location history analysis
  • Connected services discovery

  • Username Analysis

  • Cross-platform username search
  • Social media profile discovery
  • Platform correlation
  • Web presence analysis

  • Image Analysis

  • Reverse image search
  • Visual content analysis
  • Metadata extraction
  • Related image discovery

πŸ€– AI Integration

  • PANAI
  • Natural language investigation assistant
  • Automated entity extraction and relationship mapping
  • Pattern recognition and anomaly detection
  • Multi-language support
  • Context-aware suggestions
  • Timeline and graph analysis

🧩 Core Components

πŸ“¦ Entities

Entities are the fundamental building blocks of PANO. They represent distinct pieces of information that can be connected and analyzed:

  • Built-in Types
  • πŸ“§ Email: Email addresses with service detection
  • πŸ‘€ Username: Social media and platform usernames
  • 🌐 Website: Web pages with metadata
  • πŸ–ΌοΈ Image: Images with EXIF and analysis
  • πŸ“ Location: Geographic coordinates and addresses
  • ⏰ Event: Time-based occurrences
  • πŸ“ Text: Generic text content

  • Properties System

  • Type-safe property validation
  • Automatic property getters
  • Dynamic property updates
  • Custom property types
  • Metadata support

⚑ Transforms

Transforms are automated operations that process entities to discover new information and relationships:

  • Operation Types
  • πŸ” Discovery: Find new entities from existing ones
  • πŸ”— Correlation: Connect related entities
  • πŸ“Š Analysis: Extract insights from entity data
  • 🌐 OSINT: Gather open-source intelligence
  • πŸ”„ Enrichment: Add data to existing entities

  • Features

  • Async operation support
  • Progress tracking
  • Error handling
  • Rate limiting
  • Result validation

πŸ› οΈ Helpers

Helpers are specialized tools with dedicated UIs for specific investigation tasks:

  • Available Helpers
  • πŸ” Cross-Examination: Analyze statements and testimonies
  • πŸ‘€ Portrait Creator: Generate facial composites
  • πŸ“Έ Media Analyzer: Advanced image processing and analysis
  • πŸ” Base Searcher: Search near places of interest
  • πŸ”„ Translator: Translate text between languages

  • Helper Features

  • Custom Qt interfaces
  • Real-time updates
  • Graph integration
  • Data visualization
  • Export capabilities

πŸ‘₯ Contributing

We welcome contributions! To contribute to PANO:

  1. Fork the repository at https://github.com/ALW1EZ/PANO/
  2. Make your changes in your fork
  3. Test your changes thoroughly
  4. Create a Pull Request to our main branch
  5. In your PR description, include:
  6. What the changes do
  7. Why you made these changes
  8. Any testing you've done
  9. Screenshots if applicable

Note: We use a single main branch for development. All pull requests should be made directly to main.

πŸ“– Development Guide

Click to expand development documentation ### System Requirements - Operating System: Windows or Linux - Python 3.11+ - PySide6 for GUI - Internet connection for online features ### Custom Entities Entities are the core data structures in PANO. Each entity represents a piece of information with specific properties and behaviors. To create a custom entity: 1. Create a new file in the `entities` folder (e.g., `entities/phone_number.py`) 2. Implement your entity class:
from dataclasses import dataclass
from typing import ClassVar, Dict, Any
from .base import Entity

@dataclass
class PhoneNumber(Entity):
name: ClassVar[str] = "Phone Number"
description: ClassVar[str] = "A phone number entity with country code and validation"

def init_properties(self):
"""Initialize phone number properties"""
self.setup_properties({
"number": str,
"country_code": str,
"carrier": str,
"type": str, # mobile, landline, etc.
"verified": bool
})

def update_label(self):
"""Update the display label"""
self.label = self.format_label(["country_code", "number"])
### Custom Transforms Transforms are operations that process entities and generate new insights or relationships. To create a custom transform: 1. Create a new file in the `transforms` folder (e.g., `transforms/phone_lookup.py`) 2. Implement your transform class:
from dataclasses import dataclass
from typing import ClassVar, List
from .base import Transform
from entities.base import Entity
from entities.phone_number import PhoneNumber
from entities.location import Location
from ui.managers.status_manager import StatusManager

@dataclass
class PhoneLookup(Transform):
name: ClassVar[str] = "Phone Number Lookup"
description: ClassVar[str] = "Lookup phone number details and location"
input_types: ClassVar[List[str]] = ["PhoneNumber"]
output_types: ClassVar[List[str]] = ["Location"]

async def run(self, entity: PhoneNumber, graph) -> List[Entity]:
if not isinstance(entity, PhoneNumber):
return []

status = StatusManager.get()
operation_id = status.start_loading("Phone Lookup")

try:
# Your phone number lookup logic here
# Example: query an API for phone number details
location = Location(properties={
"country": "Example Country",
"region": "Example Region",
"carrier": "Example Carrier",
"source": "PhoneLookup transform"
})

return [location]

except Exception as e:
status.set_text(f"Error during phone lookup: {str(e)}")
return []

finally:
status.stop_loading(operation_id)
### Custom Helpers Helpers are specialized tools that provide additional investigation capabilities through a dedicated UI interface. To create a custom helper: 1. Create a new file in the `helpers` folder (e.g., `helpers/data_analyzer.py`) 2. Implement your helper class:
from PySide6.QtWidgets import (
QWidget, QVBoxLayout, QHBoxLayout, QPushButton,
QTextEdit, QLabel, QComboBox
)
from .base import BaseHelper
from qasync import asyncSlot

class DummyHelper(BaseHelper):
"""A dummy helper for testing"""

name = "Dummy Helper"
description = "A dummy helper for testing"

def setup_ui(self):
"""Initialize the helper's user interface"""
# Create input text area
self.input_label = QLabel("Input:")
self.input_text = QTextEdit()
self.input_text.setPlaceholderText("Enter text to process...")
self.input_text.setMinimumHeight(100)

# Create operation selector
operation_layout = QHBoxLayout()
self.operation_label = QLabel("Operation:")
self.operation_combo = QComboBox()
self.operation_combo.addItems(["Uppercase", "Lowercase", "Title Case"])
operation_layout.addWidget(self.operation_label)
operation_layout.addWidget(self.operation_combo)

# Create process button
self.process_btn = QPushButton("Process")
self.process_btn.clicked.connect(self.process_text)

# Create output text area
self.output_label = QLabel("Output:")
self.output_text = QTextEdit()
self.output_text.setReadOnly(True)
self.output_text.setMinimumHeight(100)

# Add widgets to main layout
self.main_layout.addWidget(self.input_label)
self.main_layout.addWidget(self.input_text)
self.main_layout.addLayout(operation_layout)
self.main_layout.addWidget(self.process_btn)
self.main_layout.addWidget(self.output_label)
self.main_layout.addWidget(self.output_text)

# Set dialog size
self.resize(400, 500)

@asyncSlot()
async def process_text(self):
"""Process the input text based on selected operation"""
text = self.input_text.toPlainText()
operation = self.operation_combo.currentText()

if operation == "Uppercase":
result = text.upper()
elif operation == "Lowercase":
result = text.lower()
else: # Title Case
result = text.title()

self.output_text.setPlainText(result)

πŸ“„ License

This project is licensed under the Creative Commons Attribution-NonCommercial (CC BY-NC) License.

You are free to: - βœ… Share: Copy and redistribute the material - βœ… Adapt: Remix, transform, and build upon the material

Under these terms: - ℹ️ Attribution: You must give appropriate credit - 🚫 NonCommercial: No commercial use - πŸ”“ No additional restrictions

πŸ™ Acknowledgments

Special thanks to all library authors and contributors who made this project possible.

πŸ‘¨β€πŸ’» Author

Created by ALW1EZ with AI ❀️



❌