LitterBox

Table of Contents

• Overview
• Analysis Capabilities
• Analysis Engines
• Integrated Tools
• API Reference
• Installation
• Access Methods
• Configuration
• Client Libraries
• Contributing
• Security Advisory
• Acknowledgments
• Interface

Overview

LitterBox provides a controlled sandbox environment designed for security professionals to develop and test payloads. This platform allows red teams to:

• Test evasion techniques against modern detection techniques
• Validate detection signatures before field deployment
• Analyze malware behavior in an isolated environment
• Keep payloads in-house without exposing them to external security vendors
• Ensure payload functionality without triggering production security controls

The platform includes LLM-assisted analysis capabilities through the LitterBoxMCP server, offering advanced analytical insights using natural language processing technology.

Note: While designed primarily for red teams, LitterBox can be equally valuable for blue teams by shifting perspective – using the same tools in their malware analysis workflows.

Analysis Capabilities

Initial Processing

Executable Analysis

For Windows PE files (.exe, .dll, .sys):

• Architecture identification (PE32/PE32+)
• Compilation timestamp verification
• Subsystem classification
• Entry point analysis
• Section enumeration and characterization
• Import/export table mapping

Document Analysis

For Microsoft Office files:

• Macro detection and extraction
• VBA code security analysis
• Hidden content identification
• Obfuscation technique detection

Analysis Engines

Static Analysis

• Industry-standard signature detection
• Binary entropy profiling
• String extraction and classification
• Pattern matching for known indicators

Dynamic Analysis

Available in dual operation modes:

• File Analysis: Focused on submitted samples
• Process Analysis: Targeting running processes by PID

Capabilities include:

• Runtime behavioral monitoring
• Memory region inspection and classification
• Process hollowing detection
• Code injection technique identification
• Sleep pattern analysis
• Windows telemetry collection via ETW

Doppelganger Analysis

Blender Module

Provides system-wide process comparison by:

• Collecting IOCs from active processes
• Comparing process characteristics with submitted payloads
• Identifying behavioral similarities

FuzzyHash Module

Delivers code similarity analysis through:

• Maintained database of known tools and malware
• ssdeep fuzzy hash comparison methodology
• Detailed similarity scoring and reporting

Integrated Tools

Static Analysis Suite

• YARA - Signature detection engine
• CheckPlz - AV detection testing framework
• Stringnalyzer - Advanced string analysis utility

Dynamic Analysis Suite

• YARA Memory - Runtime pattern detection
• PE-Sieve - In-memory malware detection
• Moneta - Memory region IOC analyzer
• Patriot - In-memory stealth technique detection
• RedEdr - ETW telemetry collection
• Hunt-Sleeping-Beacons - C2 beacon analyzer
• Hollows-Hunter - Process hollowing detection

API Reference

File Operations

POST   /upload                    # Upload samples for analysis
GET    /files                     # Retrieve processed file list

Analysis Endpoints

GET    /analyze/static/<hash>     # Execute static analysis
POST   /analyze/dynamic/<hash>    # Perform dynamic file analysis  
POST   /analyze/dynamic/<pid>     # Conduct process analysis

Doppelganger API

# Blender Module
GET    /doppelganger?type=blender               # Retrieve latest scan results
GET    /doppelganger?type=blender&hash=<hash>   # Compare process IOCs with payload  
POST   /doppelganger                            # Execute system scan with {"type": "blender", "operation": "scan"}

# FuzzyHash Module
GET    /doppelganger?type=fuzzy                 # Retrieve fuzzy analysis statistics
GET    /doppelganger?type=fuzzy&hash=<hash>     # Execute fuzzy hash analysis
POST   /doppelganger                            # Generate database with {"type": "fuzzy", "operation": "create_db", "folder_path": "C:\path\to\folder"}

Results Retrieval (JSON)

GET    /api/results/<hash>/info      # Retrieve file metadata
GET    /api/results/<hash>/static    # Access static analysis results
GET    /api/results/<hash>/dynamic   # Obtain dynamic analysis data
GET    /api/results/<pid>/dynamic    # Retrieve process analysis data

HTML Report Generation

GET    /api/report/          # Generate comprehensive HTML report (target = hash or pid)
GET    /api/report/?download=true  # Download report as file attachment
GET    /report/              # Download report directly (redirects to api with download=true)

Web Interface Results

GET    /results/<hash>/info      # View file information
GET    /results/<hash>/static    # Access static analysis reports
GET    /results/<hash>/dynamic   # View dynamic analysis reports
GET    /results/<pid>/dynamic    # Access process analysis reports

System Management

GET    /health                   # System health verification
POST   /cleanup                  # Remove analysis artifacts
POST   /validate/<pid>           # Verify process accessibility
DELETE /file/<hash>              # Remove specific analysis

Installation

System Requirements

• Windows operating system (Linux not supported)
• Python 3.11 or higher
• Administrator privileges

Deployment Process

1. Clone the repository:

git clone https://github.com/BlackSnufkin/LitterBox.git
cd LitterBox

2. Configure environment:

python -m venv venv
.\venv\Scripts\Activate.ps1
pip install -r requirements.txt

Operation

Standard operation:

python litterbox.py

Diagnostic mode:

python litterbox.py --debug

Access Methods

LitterBox offers three access interfaces:

• Web UI: Browser-based interface at http://127.0.0.1:1337
• API Access: Programmatic integration via Python client
• LLM Integration: AI agent interaction through MCP server

For API access, see the Client Libraries section.

Configuration

All settings are stored in config/config.yml. Edit this file to:

• Change server settings (host/port)
• Set allowed file types
• Configure analysis tools
• Adjust timeouts

Client Libraries

For programmatic access to LitterBox, use the GrumpyCats package:

GrumpyCats Documentation

The package includes:

• grumpycat.py: Dual-purpose tool that functions as:

  • Standalone CLI utility for direct server interaction
  • Python library for integrating LitterBox capabilities into custom tools

• LitterBoxMCP.py: Specialized server component that:

  • Wraps the GrumpyCat library functionality
  • Enables LLM agents to interact with the LitterBox analysis platform
  • Provides natural language interfaces to malware analysis workflows

Contributing

Development contributions should be conducted in feature branches on personal forks. For detailed contribution guidelines, refer to: CONTRIBUTING.md

Security Advisory

• DEVELOPMENT USE ONLY: This platform is designed exclusively for testing environments. Production deployment presents significant security risks.
• ISOLATION REQUIRED: Execute only in isolated virtual machines or dedicated testing environments.
• WARRANTY DISCLAIMER: Provided without guarantees; use at your own risk.
• LEGAL COMPLIANCE: Users are responsible for ensuring all usage complies with applicable laws and regulations.

Acknowledgments

This project incorporates technologies from the following contributors:

• Elastic Security
• hasherezade
• Forrest Orr
• rasta-mouse
• thefLink
• joe-desimone
• dobin
• mr.d0x

Interface