Secure Model Distribution and Deployment for LLMs

Rajashekhar Reddy Kethireddy

Authors

Rajashekhar Reddy Kethireddy Software Architect, IBM, USA Author

Keywords:

Secure Model Distribution, LLM Deployment, Encryption Techniques, Trusted Execution Environments, Homomorphic Encryption

Abstract

The proliferation of large language models (LLMs) has revolutionized natural language processing across various domains. However, the distribution and deployment of these models present significant security challenges, including risks of unauthorized access, intellectual property theft, and malicious tampering. This paper addresses the critical need for secure methodologies in the distribution and deployment of LLMs to protect both the models and their outputs. We begin by analyzing the vulnerabilities inherent in current model distribution practices, highlighting potential attack vectors such as interception during transmission and reverse engineering from deployed applications. To mitigate these risks, we propose a comprehensive security framework that incorporates advanced encryption techniques, secure multi-party computation, and hardware-based Trusted Execution Environments (TEEs). Our framework ensures that models remain confidential during distribution and are executed in secure environments that prevent unauthorized access. Furthermore, we introduce protocols for authentication and authorization that verify the integrity of models before deployment. These protocols leverage cryptographic signatures and certificates to establish trust between developers and deployment platforms. We also explore the use of homomorphic encryption to allow computations on encrypted models, thereby reducing the exposure of sensitive model parameters. Through empirical evaluations, we demonstrate that our security measures impose minimal performance overhead while significantly enhancing protection against common threats. Case studies in cloud-based and edge deployments illustrate the practicality and scalability of our approach. In conclusion, securing the distribution and deployment of LLMs is essential for maintaining trust and safeguarding intellectual property in AI applications. Our proposed solutions offer a balanced trade-off between security and efficiency, providing a viable pathway for organizations to deploy LLMs securely in various operational environments.

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