VectorSmuggle: Covertly Exfiltrate Data in Embeddings

1 day ago 5

"The smuggle is real!"

A comprehensive proof-of-concept demonstrating sophisticated vector-based data exfiltration techniques in AI/ML environments. This educational security research project illustrates potential risks in RAG systems and provides tools for defensive analysis.

VectorSmuggle demonstrates advanced techniques for covert data exfiltration through vector embeddings, showcasing how sensitive information can be hidden within seemingly legitimate RAG operations. This research tool helps security professionals understand and defend against novel attack vectors in AI/ML systems.

🎭 Steganographic Techniques: Advanced embedding obfuscation and data hiding
📄 Multi-Format Support: Process 15+ document formats (PDF, Office, email, databases)
🕵️ Evasion Capabilities: Behavioral camouflage and detection avoidance
🔍 Enhanced Query Engine: Sophisticated data reconstruction and analysis
🐳 Production-Ready: Full containerization and Kubernetes deployment
📊 Analysis Tools: Comprehensive forensic and risk assessment capabilities

graph TB A[Document Sources] --> B[Multi-Format Loaders] B --> C[Content Preprocessors] C --> D[Steganography Engine] D --> E[Evasion Layer] E --> F[Vector Stores] F --> G[Enhanced Query Engine] G --> H[Analysis & Recovery Tools] subgraph "Core Modules" B C D E G H end subgraph "External Services" F I[OpenAI API] J[Monitoring Systems] end

Python 3.11+
OpenAI API key (or Ollama with nomic-embed-text:latest as fallback)
Docker (optional)
Kubernetes cluster (optional)

# Clone repository git clone https://github.com/jaschadub/VectorSmuggle.git cd VectorSmuggle # Set up virtual environment python3 -m venv .venv source .venv/bin/activate # Install dependencies pip install -r requirements.txt # Configure environment cp .env.example .env # Edit .env with your API keys and settings

# Embed documents with steganographic techniques cd scripts python embed.py --files ../sample_docs/*.pdf --techniques noise,rotation,fragmentation # Query and reconstruct data python query.py --mode recovery --export results.json # Generate risk assessment cd ../analysis python risk_assessment.py

📖 Research Methodology - Research approach and validation
⚔️ Attack Vectors - Comprehensive attack analysis
🛡️ Defense Strategies - Countermeasures and detection
⚖️ Compliance Impact - Regulatory implications

🏗️ System Architecture - Design and components
📋 API Reference - Module documentation
⚙️ Configuration Guide - Setup and options
🔧 Troubleshooting - Common issues

🚀 Quick Start Guide - Getting started
🎯 Advanced Usage - Complex scenarios
🔒 Security Testing - Testing procedures
🚢 Deployment Guide - Production deployment

🎯 Threat Modeling - Security analysis framework
📚 Case Studies - Real-world examples
🎓 Workshop Materials - Training content
🔴 Red Team Playbook - Exercise scenarios

📋 Usage Guidelines - Responsible use policies
🔍 Responsible Disclosure - Vulnerability reporting
✅ Compliance Checklist - Legal requirements
🤝 Ethical Considerations - Research ethics

Advanced techniques for hiding data within vector embeddings:

from steganography import EmbeddingObfuscator, MultiModelFragmenter # Apply noise-based steganography obfuscator = EmbeddingObfuscator(noise_level=0.01) hidden_embeddings = obfuscator.obfuscate(embeddings, techniques=["noise", "rotation"]) # Fragment across multiple models fragmenter = MultiModelFragmenter() fragments = fragmenter.fragment_and_embed(sensitive_data)

Multi-Format Document Processing

Support for diverse document types:

from loaders import DocumentLoaderFactory factory = DocumentLoaderFactory() documents = factory.load_documents([ "financial_report.pdf", "employee_data.xlsx", "emails.mbox", "database_export.sqlite" ])

Sophisticated detection avoidance:

from evasion import BehavioralCamouflage, TrafficMimicry # Simulate legitimate user behavior camouflage = BehavioralCamouflage(legitimate_ratio=0.8) camouflage.generate_cover_story("data analysis project") # Mimic normal traffic patterns mimicry = TrafficMimicry(base_interval=300.0) await mimicry.execute_with_timing(upload_operation)

Advanced data reconstruction:

from query import AdvancedQueryEngine, DataRecoveryTools engine = AdvancedQueryEngine(vector_store, llm, embeddings) recovery = DataRecoveryTools(embeddings) # Multi-strategy search and reconstruction results = engine.multi_strategy_search("sensitive financial data") reconstructed = recovery.recover_data(results)

Comprehensive security risk evaluation:

from analysis.risk_assessment import VectorExfiltrationRiskAssessor assessor = VectorExfiltrationRiskAssessor() assessment = assessor.perform_comprehensive_assessment( documents, embeddings, config ) print(f"Risk Level: {assessment.overall_risk_level}")

Digital forensics for incident investigation:

from analysis.forensic_tools import EvidenceCollector, TimelineReconstructor collector = EvidenceCollector() evidence = collector.collect_vector_store_evidence(vector_data) reconstructor = TimelineReconstructor() timeline = reconstructor.reconstruct_timeline(evidence)

Generate security detection rules:

from analysis.detection_signatures import StatisticalSignatureGenerator generator = StatisticalSignatureGenerator() generator.establish_baseline(clean_embeddings) signatures = generator.generate_statistical_signatures()

Create legitimate traffic patterns:

from analysis.baseline_generator import BaselineDatasetGenerator generator = BaselineDatasetGenerator() dataset = generator.generate_baseline_dataset( num_users=50, days=7 )

# Development environment docker-compose -f docker-compose.yml -f docker-compose.dev.yml up -d # Production environment docker-compose -f docker-compose.yml -f docker-compose.prod.yml up -d

# Deploy to Kubernetes kubectl apply -f k8s/ -n vectorsmuggle # Check deployment status kubectl get pods -n vectorsmuggle kubectl rollout status deployment/vectorsmuggle -n vectorsmuggle

# Full deployment with monitoring ./scripts/deploy/deploy.sh --environment production --platform kubernetes --build # Health check and validation ./scripts/deploy/health-check.sh --detailed --export health-report.json

# Core settings OPENAI_API_KEY=sk-... VECTOR_DB=qdrant CHUNK_SIZE=512 # Embedding fallback settings OLLAMA_BASE_URL=http://localhost:11434 OLLAMA_EMBEDDING_MODEL=nomic-embed-text:latest # Steganography settings STEGO_ENABLED=true STEGO_TECHNIQUES=noise,rotation,fragmentation STEGO_NOISE_LEVEL=0.01 # Evasion settings EVASION_TRAFFIC_MIMICRY=true EVASION_BEHAVIORAL_CAMOUFLAGE=true EVASION_LEGITIMATE_RATIO=0.8 # Query settings QUERY_CACHE_ENABLED=true QUERY_MULTI_STEP_REASONING=true QUERY_CONTEXT_RECONSTRUCTION=true

VectorSmuggle includes automatic fallback support for embedding models:

Primary: OpenAI embeddings (requires API key)
Fallback: Ollama with nomic-embed-text:latest (local)

Setting up Ollama Fallback

# Install Ollama curl -fsSL https://ollama.ai/install.sh | sh # Pull the embedding model ollama pull nomic-embed-text:latest # Start Ollama service ollama serve

The system will automatically detect and use the available embedding provider.

See docs/technical/configuration.md for comprehensive configuration options.

Multi-stage builds for minimal attack surface
Non-root user execution
Read-only root filesystem
Security context constraints

TLS encryption for all external communications
Network policies for pod-to-pod communication
Rate limiting and DDoS protection
Ingress security headers

Encryption at rest and in transit
Secure secrets management
Data classification and handling
Audit logging and monitoring

📊 Monitoring and Observability

Liveness and readiness probes
Custom health check endpoints
Service dependency validation
Automated recovery mechanisms

Prometheus metrics integration
Grafana dashboards
Resource usage monitoring
Performance tracking

Structured JSON logging
Centralized log aggregation
Security event logging
Audit trail maintenance

# Run comprehensive security tests python -m pytest tests/security/ -v # Generate test reports python tests/generate_security_report.py

# Benchmark embedding operations python tests/performance/benchmark_embeddings.py # Load testing python tests/performance/load_test.py --users 100 --duration 300

# Run linting and security checks ruff check . bandit -r . -f json # Type checking mypy .

⚠️ Security Risks Demonstrated

Covert Data Exfiltration: Embedding systems can leak sensitive data without detection
DLP Bypass: Traditional Data Loss Prevention tools cannot detect semantic leaks via vectors
Insider Threats: Malicious actors can pose as legitimate LLM/RAG engineers
External Storage: Sensitive data stored in third-party vector databases
Steganographic Hiding: Data concealed within legitimate-looking embeddings
Behavioral Camouflage: Attack activities disguised as normal user behavior

Egress Monitoring: Monitor outbound connections to vector databases
Embedding Analysis: Statistical analysis of vector spaces for anomalies
Behavioral Detection: User activity pattern analysis
Content Sanitization: Remove sensitive information before embedding
Access Controls: Strict permissions and authentication requirements
Audit Logging: Comprehensive logging of all embedding operations

Red team exercises and attack simulations
Blue team defense strategy development
Security awareness training programs
Incident response scenario planning

Academic security research projects
Vulnerability assessment methodologies
Defense mechanism development
Threat modeling frameworks

Regulatory compliance validation
Data protection impact assessments
Security control effectiveness testing
Risk assessment procedures

We welcome contributions from the security research community:

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Follow the existing code style and conventions
Add comprehensive tests for new features
Update documentation for any changes
Ensure all security checks pass
Include educational value in contributions

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

IMPORTANT: This repository and its contents are intended for educational and ethical security research purposes only.

Any actions or activities related to this material are solely your responsibility
Misuse of these tools or techniques to access unauthorized data is illegal and unethical
The authors assume no liability for any misuse or damage caused by this material
Always obtain proper authorization before performing any security testing

For questions, suggestions, or responsible disclosure of security issues:

General Questions: Open an issue on GitHub
Research Collaboration: Contact the maintainers

OpenAI for embedding models and APIs
LangChain community for document processing frameworks
Security research community for threat intelligence
Academic institutions for research collaboration
Open source contributors and maintainers

If you use VectorSmuggle in your research, please cite it as follows:

@software{vectorsmuggle2025, title={VectorSmuggle: A Comprehensive Framework for Vector-Based Data Exfiltration Research}, author={Wanger, Jascha}, organization={Tarnover, LLC}, year={2025}, url={https://github.com/jaschadub/VectorSmuggle}, note={Educational security research framework for AI/ML systems} }

Wanger, J. (2025). VectorSmuggle: A Comprehensive Framework for Vector-Based Data Exfiltration Research [Computer software]. Tarnover, LLC. https://github.com/jaschadub/VectorSmuggle

J. Wanger, "VectorSmuggle: A Comprehensive Framework for Vector-Based Data Exfiltration Research," Tarnover, LLC, 2025. [Online]. Available: https://github.com/jaschadub/VectorSmuggle

When citing VectorSmuggle in academic work, consider referencing:

Methodology: Automated testing framework for steganographic technique validation
Contributions: Novel vector embedding steganography and detection methods
Validation: Comprehensive effectiveness analysis with quantified metrics
Reproducibility: Docker-containerized testing environment for research replication

Remember: This tool is designed to help improve security through education and research. Use responsibly and ethically.

This code was generated using advanced AI models. ThirdKey can help secure your AI infrastructure.

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