Login
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.
FreshRSS # Node.js 14+ is required
node -v
docker-compose build --no-cache
docker-compose up -d
Now open your browser: http://localhost:8080/
To stop the docker container
docker-compose down
# 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
# Build the application
npm run build
# Preview production build
npm run preview
Progress bar shows upload and analysis status
Analysis Views
Real-time updates as you navigate
Search Functions
Results are highlighted in the current view
String Analysis
git checkout -b feature/AmazingFeature)git commit -m 'Add some AmazingFeature')git push origin feature/AmazingFeature)This project is licensed under the MIT License - see the LICENSE.md file for details.
Clone the repository: bash git clone https://github.com/ALW1EZ/PANO.git cd PANO
Run the application:
./start_pano.sh
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;
source venv/bin/activate
call venv\Scripts\activate
Visual node and edge styling
Timeline Analysis
Temporal relationship analysis
Map Integration
Connected services discovery
Username Analysis
Web presence analysis
Image Analysis
Entities are the fundamental building blocks of PANO. They represent distinct pieces of information that can be connected and analyzed:
📝 Text: Generic text content
Properties System
Transforms are automated operations that process entities to discover new information and relationships:
🔄 Enrichment: Add data to existing entities
Features
Helpers are specialized tools with dedicated UIs for specific investigation tasks:
🔄 Translator: Translate text between languages
Helper Features
We welcome contributions! To contribute to PANO:
Note: We use a single
mainbranch for development. All pull requests should be made directly tomain.
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"])
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)
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)
This project is licensed under the Creative Commons Attribution-NonCommercial (CC BY-NC) License.
You are free to: - ✅ Share: Copy and redistribute the material - ✅ Adapt: Remix, transform, and build upon the material
Under these terms: - ℹ️ Attribution: You must give appropriate credit - 🚫 NonCommercial: No commercial use - 🔓 No additional restrictions
Special thanks to all library authors and contributors who made this project possible.
Created by ALW1EZ with AI ❤️
Evade EDR's the simple way, by not touching any of the API's they hook.
I've noticed that most EDRs fail to scan scripting files, treating them merely as text files. While this might be unfortunate for them, it's an opportunity for us to profit.
Flashy methods like residing in memory or thread injection are heavily monitored. Without a binary signed by a valid Certificate Authority, execution is nearly impossible.
Enter BYOSI (Bring Your Own Scripting Interpreter). Every scripting interpreter is signed by its creator, with each certificate being valid. Testing in a live environment revealed surprising results: a highly signatured PHP script from this repository not only ran on systems monitored by CrowdStrike and Trellix but also established an external connection without triggering any EDR detections. EDRs typically overlook script files, focusing instead on binaries for implant delivery. They're configured to detect high entropy or suspicious sections in binaries, not simple scripts.
This attack method capitalizes on that oversight for significant profit. The PowerShell script's steps mirror what a developer might do when first entering an environment. Remarkably, just four lines of PowerShell code completely evade EDR detection, with Defender/AMSI also blind to it. Adding to the effectiveness, GitHub serves as a trusted deployer.
The PowerShell script achieves EDR/AV evasion through four simple steps (technically 3):
1.) It fetches the PHP archive for Windows and extracts it into a new directory named 'php' within 'C:\Temp'.
2.) The script then proceeds to acquire the implant PHP script or shell, saving it in the same 'C:\Temp\php' directory.
3.) Following this, it executes the implant or shell, utilizing the whitelisted PHP binary (which exempts the binary from most restrictions in place that would prevent the binary from running to begin with.)
With these actions completed, congratulations: you now have an active shell on a Crowdstrike-monitored system. What's particularly amusing is that, if my memory serves me correctly, Sentinel One is unable to scan PHP file types. So, feel free to let your imagination run wild.
I am in no way responsible for the misuse of this. This issue is a major blind spot in EDR protection, i am only bringing it to everyones attention.
A big thanks to @im4x5yn74x for affectionately giving it the name BYOSI, and helping with the env to test in bringing this attack method to life.
It appears as though MS Defender is now flagging the PHP script as malicious, but still fully allowing the Powershell script full execution. so, modify the PHP script.
hello sentinel one :) might want to make sure that you are making links not embed.
