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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18995804 |
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| _version_ | 1866901062783336448 |
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| author | Weng, Jiasi Weng, Jian Li, Ming |
| author_facet | Weng, Jiasi Weng, Jian Li, Ming |
| contents | <p>This paper proposes a built-in framework that embeds a dedicated “Crypto Expert” directly into large language models (LLMs) architecture. As an initial attempt, we design a differentiable proxy tailored to the Advanced Encryption Standard (AES) algorithm, using our customized neuron units, including SoftXOR, SoftLUT and GF-conv neurons. These units provide functional equivalence to the AES within the Boolean domain, while enabling stable gradients for backpropagation. By integrating this differentiable proxy as a specialized expert within a Mixture-of-Expert (MoE) LLM, the LLM learns to autonomously route and encrypt sensitive tokens during the training phase. After training, the differentiable proxy is seamlessly swapped for a real and discrete AES implementation to guarantee provable security at inference. Our empirical evaluations demonstrate that our approach significantly reduces neuron counts and latency compared to prior ReLU-based representation, mitigates continuous differential attacks, and enforces end-to-end data protection without degrading downstream task utility. We expect this attempt to serve as a catalyst for future research into the seamless fusion of formal cryptographic guarantees and deep learning computation graphs.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18995804 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Built-in Crypto Expert for Artificial Intelligence: How Far is the Horizon? Weng, Jiasi Weng, Jian Li, Ming <p>This paper proposes a built-in framework that embeds a dedicated “Crypto Expert” directly into large language models (LLMs) architecture. As an initial attempt, we design a differentiable proxy tailored to the Advanced Encryption Standard (AES) algorithm, using our customized neuron units, including SoftXOR, SoftLUT and GF-conv neurons. These units provide functional equivalence to the AES within the Boolean domain, while enabling stable gradients for backpropagation. By integrating this differentiable proxy as a specialized expert within a Mixture-of-Expert (MoE) LLM, the LLM learns to autonomously route and encrypt sensitive tokens during the training phase. After training, the differentiable proxy is seamlessly swapped for a real and discrete AES implementation to guarantee provable security at inference. Our empirical evaluations demonstrate that our approach significantly reduces neuron counts and latency compared to prior ReLU-based representation, mitigates continuous differential attacks, and enforces end-to-end data protection without degrading downstream task utility. We expect this attempt to serve as a catalyst for future research into the seamless fusion of formal cryptographic guarantees and deep learning computation graphs.</p> |
| title | A Built-in Crypto Expert for Artificial Intelligence: How Far is the Horizon? |
| url | https://doi.org/10.5281/zenodo.18995804 |