Zihao Guan

SLIM: Secure and Efficient Inference for Large Language Models on Untrusted Devices via TEEs

Deploying large language models (LLMs) on untrusted hardware entails a risk of weight extraction, which can lead to unauthorized replication and misuse of the model. A practical approach is to leverage Trusted Execution Environments (TEEs) and protect model security by obfuscating model weights. How…