LitterBox v1.0
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@@ -13,53 +13,59 @@ The platform provides automated analysis through an intuitive web interface, mon
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This ensures your payloads work as intended before execution in target environments.
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## Core Features
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## Features
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### Initial Analysis
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Upon file upload, LitterBox automatically performs:
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- File identification and hashing (MD5, SHA256)
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- Shannon entropy calculation
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- File type detection and MIME analysis
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- File identification with multiple hashing algorithms (MD5, SHA256)
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- Shannon entropy calculation for encryption detection
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- Advanced file type detection and MIME analysis
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- Original filename preservation
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- Upload timestamp recording
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- Upload timestamp tracking
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### PE File Analysis
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For executables (.exe, .dll, .sys):
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- File type detection (PE32/PE32+)
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- Machine type identification
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- Compilation timestamp extraction
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- Subsystem identification
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- Entry point location
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- Section enumeration
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- Import DLL listing
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For Windows executables (.exe, .dll, .sys):
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- PE file type detection (PE32/PE32+)
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- Machine architecture identification
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- Compilation timestamp analysis
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- Subsystem classification
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- Entry point detection
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- Section enumeration and analysis
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- Import DLL dependency mapping
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### Office Document Analysis
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For Office files (.docx, .xlsx, .doc, .xls, .xlsm, .docm):
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- Macro detection
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- VBA code analysis (if macros present)
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For Microsoft Office files (.docx, .xlsx, .doc, .xls, .xlsm, .docm):
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- Macro detection and extraction
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- VBA code analysis
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- Hidden content identification
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## Analysis Options
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## Analysis Capabilities
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### Static Analysis
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- Scanning binaries against known detection signatures and rulesets
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- Analyzing file characteristics and entropy levels for suspicious indicators
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- Strings analyzing to locate strings that can serve as suspicious indicators
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### Static Analysis Engine
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- Signature-based detection using industry-standard rulesets
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- Binary entropy analysis
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- String extraction and analysis
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- Pattern matching for suspicious indicators
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### Dynamic Analysis
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Supports two modes: File, PID
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- Scanning executable files and processes to identify suspicious behavioral characteristics
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- Inspecting memory regions to detect anomalous content and hidden payloads
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- Analyzing process hollowing and injection techniques for detection artifacts
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- Monitoring sleep patterns of a beacon processes
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- Validating integrity of PE files and detecting runtime modifications
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### Dynamic Analysis Engine
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Available in two modes:
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- File Analysis Mode
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- Process ID (PID) Analysis Mode
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Features include:
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- Behavioral monitoring
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- Memory region inspection
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- Process hollowing detection
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- Injection technique analysis
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- Sleep pattern monitoring
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- PE integrity verification
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## Integrated Tools
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### Static Analyzers
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### Static Analysis Suite
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- [YARA](https://github.com/elastic/protections-artifacts/tree/main/yara) - Pattern matching and signature detection
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- [CheckPlz](https://github.com/BlackSnufkin/CheckPlz) - AV detection testing
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### Dynamic Analyzers
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### Dynamic Analysis Suite
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- [YARA](https://github.com/elastic/protections-artifacts/tree/main/yara) (memory scanning) - Runtime pattern detection
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- [PE-Sieve](https://github.com/hasherezade/pe-sieve) - Process and memory inspection
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- [Moneta](https://github.com/forrest-orr/moneta) - Sleep pattern analysis
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@@ -82,31 +88,33 @@ GET /health # System health and tool status check
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POST /cleanup # Clean analysis artifacts and uploads
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POST /validate/<pid> # Validate process accessibility
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```
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## Installation
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## Usage
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### Setup
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### Prerequisites
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- Python 3.8 or higher
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- Administrator privileges (required for certain features)
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- Windows operating system (required for specific analyzers)
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### Setup Steps
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1. Clone the repository:
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```bash
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git clone https://github.com/your-repo/litterbox.git
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cd litterbox
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```
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2. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Edit `config/config.yaml` to specify file paths and tool settings.
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```bash
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git clone https://github.com/your-repo/litterbox.git
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cd litterbox
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```
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2. Install required dependencies:
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```bash
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pip install -r requirements.txt
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```
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### Run
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### Running LitterBox
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```bash
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python litterbox.py
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```
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- Accessible at `http://127.0.0.1:1337`
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- Requires admin privileges for some features.
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The web interface will be available at: `http://127.0.0.1:1337`
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## Configuration
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+3
-1
@@ -1,3 +1,4 @@
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# app/__init__.py
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from flask import Flask
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import yaml
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import os
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@@ -13,7 +14,8 @@ def create_app():
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# Load configuration from YAML
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config = load_config()
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app.config.update(config)
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app.name = config['application']['name']
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# Ensure upload and temp directories exist
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for folder in [config['upload']['upload_folder']]:
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os.makedirs(folder, exist_ok=True)
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@@ -12,7 +12,6 @@ def is_running_as_admin():
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return os.geteuid() == 0 # Unix/Linux admin check (root user)
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app = create_app()
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app.name = 'LitterBox'
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if __name__ == '__main__':
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if not is_running_as_admin():
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