Logic-Locked Accelerators Enhance IP Protection for Generative AI

Global: Logic-Locked Accelerators Offer New IP Protection for Generative AI Models

A team of researchers announced a new intellectual property protection scheme for generative AI models on Dec. 26, 2025. The approach, called Logic-Locked Accelerators (LLA), combines hardware and software techniques to defend against model theft, corruption, and information leakage. By embedding secret key bits into neural network neurons and integrating a locking module into AI accelerators, the system requires a pre‑stored secret key to unlock model services. The work was submitted to the arXiv preprint server under the categories of cryptography and security, artificial intelligence, and machine learning.

Software-Level Key Embedding

LLA embeds key bits directly into selected neurons so that incorrect keys trigger outlier activations, causing a noticeable degradation in model performance. To conceal the key values, the authors apply invariance transformations that make the embedded bits indistinguishable from normal model parameters, thereby resisting reverse‑engineering attempts.

Hardware Integration

The scheme incorporates a lightweight locking module within the AI accelerator’s dataflow pipeline. This module checks the stored secret key before allowing inference operations to proceed, while remaining compatible with a range of existing dataflow patterns and toolchains. The design aims to preserve the accelerator’s flexibility for diverse model architectures.

Security Evaluation

Experimental results indicate that LLA can withstand a broad spectrum of oracle‑guided key optimization attacks. The authors evaluated the system against adaptive adversaries who iteratively query the model to infer key bits, finding that the embedded protections effectively block key recovery.

Performance Impact

Despite the added security mechanisms, the reported computational overhead is minimal—less than 0.1% for a configuration using 7,168 key bits. This low overhead suggests that the approach can be deployed in production environments without compromising throughput.

Potential Applications and Future Work

By requiring a pre‑stored secret key as a license, LLA enables model owners to control access to their services across the supply chain. The authors propose extending the framework to support dynamic key updates and to explore its applicability to other AI hardware platforms.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.