arXiv:2606.16352v1 Announce Type: cross Abstract: Computation integrity of remote large language model (LLM) serving can be questionable. For conventional deep neural networks (DNNs), the existing TEE-shielded DNN partitioning (TSDP) approach uses Trusted Execution Environment (TEE) to compute non-linear components and verify the integrity of linear components offloaded to an untrusted GPU. However, directly applying TSDP to Transformer-based LLMs incurs significant TEE computation and TEE-GPU communication overhead. This paper presents Communication-efficient TEE-GPU Attention (\textsc{VeriAt
The increasing reliance on remote LLM serving and the growing concerns around data integrity and security within AI inference necessitate robust verification methods.
Ensuring the integrity of LLM outputs, especially in sensitive applications, is crucial for trust and widespread adoption, impacting enterprise and national security deployments.
This research introduces a more efficient method for verifying LLM inference within Trusted Execution Environments, potentially reducing the computational and communication overhead previously seen with conventional deep neural networks.
- · Cloud providers offering secure AI inference
- · Enterprises deploying LLMs in sensitive domains
- · Developers of Trusted Execution Environments
- · AI security solution providers
- · Providers of LLM services with weak integrity guarantees
- · Users vulnerable to compromised AI model outputs
Improved security and trustworthiness of remote LLM inference.
Accelerated adoption of LLMs in highly regulated or critical sectors due to enhanced integrity guarantees.
Increased demand for TEE-compatible hardware and software, potentially leading to new industry standards for verifiable AI compute.
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Read at arXiv cs.AI