Future of Life Institute's research with Mithril Security

Exploration of secure hardware solutions for safe AI deployment - Future of Life Institute 
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 This collaboration between the Future of Life Institute and Mithril Security explores hardware-backed AI governance tools for transparency, traceability, and confidentiality. Published: November 30, 2023 Author: Future of Life Institute Contents 

 Introduction

 AI safety has become a key subject with the recent progress of AI. Debates on the topic have helped outline desirable properties a safe AI should follow, such as provenance (where does the model come from), confidentiality (how to ensure the confidentiality of prompts or of the model weights), or transparency (how to know what model is used on data).

 While such discussions have been necessary to define what properties such models should have, they are not sufficient, as there are few technical solutions to actually guarantee that those properties are implemented in production. 

 See our other post with Mithril Security on verifiable training of AI models .

 For instance, there is no way to guarantee that a good actor who trained an AI satisfying some safety requirements has actually deployed that same model, nor is it possible to detect if a malicious actor is serving a harmful model. This is due to the lack of transparency and technical proof that a specific and trustworthy model is indeed loaded in the backend.

 This need for technical answers to the AI governance challenges has been expressed in the highest spheres. For instance, the White House Executive Order on AI Safety and Security has highlighted the need to develop privacy-preserving technologies and the importance of having confidential, transparent, and traceable AI systems.

 Hardware-backed security today

 Fortunately, modern techniques in cryptography and secure hardware technology provide the building blocks to provide verifiable systems that can enforce AI governance policies. For example, unfalsifiable cryptographic proof can be created to attest that a model comes from the application of a specific code on a specific dataset. This could prevent copyright issues, or prove that a certain number of training epochs were done, verifying whether a threshold in compute has or has not been breached. 

 The field of secure hardware has been evolving and has reached a stage where it can be used in production to make AI safer. While initially developed for users’ devices (e.g. iPhones use secure enclaves to securely store and process biometric data), large server-side processors have become mature enough to tackle AI workloads.

 While recent cutting-edge AI hardware, such as Intel Xeon with Intel SGX or Nvidia H100s with Confidential Computing , possess the hardware features to implement AI governance properties, few projects have emerged yet to leverage them to build AI governance tooling.

 Proof-of-concept: Secure AI deployment

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