Trusted Execution Environments (TEEs) enable secure and private large language model (LLM) inference, ensuring that sensitive computations occur within an encrypted and isolated environment.

​
Core functionality

The core functionality focuses on securely performing privacy-preserving computations, including private LLM inference, while ensuring data confidentiality and integrity at every stage of execution.
  • Secure model loading: Loads encrypted models into the TEE, preventing unauthorized access.
  • Protected inference: Runs entire inference workflows securely within the enclave.
  • Secure output handling: Ensures results remain encrypted and protected during transmission and storage.

​
Key components

The key components enable the secure management and execution of model inference workflows, ensuring efficiency, accuracy, and robust protection throughout the process.

​
Model management

  • Encrypted model storage: Stores models in encrypted format, preventing unauthorized access.
  • Secure weight loading: Loads model weights securely into the enclave to ensure integrity and authenticity.
  • Version control & updates: Implements secure version tracking and controlled updates to prevent tampering.

​
Inference pipeline

  • Input preprocessing in TEE: Cleans, formats, and normalizes incoming data before inference.
  • Batched inference processing: Groups multiple requests to optimize efficiency and resource usage.
  • Output post-processing: Ensures results remain encrypted, preventing data leaks or unauthorized exposure.

​
Security measures

  • Model encryption at rest: Encrypts model weights before and after inference to prevent unauthorized access.
  • Secure inference runtime: Ensures that inference operations only execute within verified TEEs.
  • Memory protection: Prevents unauthorized memory access, mitigating side-channel attacks.
The following Go structs define the TEE-based LLM inference architecture, ensuring secure model execution and data protection. 
type LLMInference struct {
    ModelID     string
    ModelConfig ModelSettings
    Runtime     TEERuntime
}

type ModelSettings struct {
    // Model configuration
    BatchSize      int
    Precision      string
    MaxInputLength int
    // Security settings
    EncryptionKey  []byte
    AccessControl  Policy
}

type InferenceRequest struct {
    // Input data for inference
    Input []byte
    // Metadata for processing
    Metadata RequestMetadata
    // Security context
    SecurityContext Context
}