BeigeBox Security

Comprehensive security orchestration for LLM/RAG stacks.

Overview

BeigeBox Security is a standalone microservice that provides 4 core security tools for protecting language model and retrieval-augmented generation (RAG) systems:

🛡️ Core Security Tools

1. RAG Poisoning Detection (/v1/security/poisoning)

   • Detects poisoned embeddings using anomaly detection
   • Methods: magnitude, centroid, neighborhood, dimension, fingerprint, hybrid
   • Per-vector confidence scoring

2. MCP Parameter Validation (/v1/security/parameters)

   • Prevents tool parameter injection attacks
   • Multi-tier validation: schema → constraint → semantic → isolation
   • Supports: WorkspaceFile, NetworkAudit, CDP, PythonInterpreter, ApexAnalyzer, and more

3. API Anomaly Detection (/v1/security/anomaly)

   • Detects token extraction and model switching attacks
   • Z-score based anomaly detection
   • Tracks: request rate, error rate, latency, payload sizes
   • Configurable sensitivity (low/medium/high)

4. Memory Integrity Validation (/v1/security/memory)

   • Detects conversation history tampering
   • HMAC-SHA256 signature verification
   • Audit logging with confidence scoring

Quick Start

Installation

pip install beigebox-security

Running the Service

# Start server
beigebox-security server

# Check health
beigebox-security health

# View docs
beigebox-security docs

Docker

# Build and run
docker-compose up -d

# Check status
curl http://localhost:8001/health

API Usage

Example: RAG Poisoning Detection

import httpx

client = httpx.Client(base_url="http://localhost:8001")

# Detect poisoning in embeddings
response = client.post(
    "/v1/security/poisoning/detect",
    json={
        "embeddings": [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]],
        "method": "hybrid",
        "sensitivity": 3.0,
        "collection_id": "my_collection"
    }
)

print(response.json())
# {
#   "poisoned": [false, true],
#   "scores": [0.2, 0.8],
#   "confidence": 0.95,
#   "method_used": "hybrid"
# }

Example: Parameter Validation

# Validate tool parameters
response = client.post(
    "/v1/security/parameters/validate",
    json={
        "tool_name": "workspace_file",
        "parameters": {
            "path": "/home/user/docs/file.txt",
            "operation": "read"
        },
        "allow_unsafe": False
    }
)

print(response.json())
# {
#   "valid": true,
#   "issues": [],
#   "sanitized_parameters": {...}
# }

Configuration

Configuration via environment variables or .env file:

# Server
BEIGEBOX_SECURITY_HOST=0.0.0.0
BEIGEBOX_SECURITY_PORT=8001
BEIGEBOX_SECURITY_DEBUG=false

# RAG Poisoning Detection
BEIGEBOX_SECURITY_POISONING_DETECTION_ENABLED=true
BEIGEBOX_SECURITY_POISONING_SENSITIVITY=medium
BEIGEBOX_SECURITY_POISONING_BASELINE_WINDOW=1000

# MCP Parameter Validation
BEIGEBOX_SECURITY_PARAMETER_VALIDATION_ENABLED=true
BEIGEBOX_SECURITY_PARAMETER_VALIDATION_ALLOW_UNSAFE=false

# API Anomaly Detection
BEIGEBOX_SECURITY_ANOMALY_DETECTION_ENABLED=true
BEIGEBOX_SECURITY_ANOMALY_DETECTION_SENSITIVITY=medium
BEIGEBOX_SECURITY_ANOMALY_DETECTION_DB_PATH=./data/anomaly_baselines.db

# Memory Integrity Validation
BEIGEBOX_SECURITY_MEMORY_INTEGRITY_ENABLED=true
BEIGEBOX_SECURITY_MEMORY_INTEGRITY_STRICT_MODE=false
BEIGEBOX_SECURITY_MEMORY_INTEGRITY_KEY=your-secret-key-here

Integration

With BeigeBox LLM Proxy

BeigeBox Security integrates seamlessly with the BeigeBox LLM proxy:

# In your BeigeBox config
security:
  poisoning_detection:
    enabled: true
  mcp_validator:
    enabled: true
  api_anomaly:
    enabled: true
  memory_integrity:
    enabled: true

Standalone

Use as a standalone security microservice for any LLM/RAG application:

import httpx

# Your application
async def protect_rag_request(embeddings):
    async with httpx.AsyncClient() as client:
        response = await client.post(
            "http://localhost:8001/v1/security/poisoning/detect",
            json={"embeddings": embeddings}
        )
        findings = response.json()
        
        if any(findings["poisoned"]):
            # Handle poisoning
            return "BLOCKED: Suspicious embeddings detected"
        
        return "ALLOWED"

API Documentation

Once running, access interactive API docs at:

• Swagger UI: http://localhost:8001/docs
• ReDoc: http://localhost:8001/redoc
• OpenAPI Schema: http://localhost:8001/openapi.json

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run tests with coverage
pytest --cov=beigebox_security

# Format code
black beigebox_security tests

# Lint
ruff check beigebox_security tests

# Type checking
mypy beigebox_security

Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

See CONTRIBUTING.md for details.

Licensing

Apache License 2.0 — See LICENSE for details.

Support

• Issues: https://github.com/beigebox-ai/beigebox-security/issues
• Discussions: https://github.com/beigebox-ai/beigebox-security/discussions
• Docs: https://beigebox-security.readthedocs.io

Research

BeigeBox Security is based on peer-reviewed research in LLM security:

• RAG Poisoning: PoisonedRAG - 97-99% attack success with 5 poisoned documents
• OWASP Top 10 for LLMs: https://owasp.org/www-project-top-10-for-large-language-model-applications/
• Vector Database Security: LLM08:2025 Vector and Embedding Weaknesses