LoREnc: Low-Rank Encryption for Securing Foundation Models and LoRA Adapters
Summary
LoREnc is a training-free framework that secures foundation models and LoRA adapters via spectral truncation and compensation, preventing unauthorized model recovery while maintaining performance for authorized users. Accepted at ICIP 2026.
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Paper page - LoREnc: Low-Rank Encryption for Securing Foundation Models and LoRA Adapters
Source: https://huggingface.co/papers/2605.13163
Abstract
LoREnc secures foundation models and low-rank adapters through spectral truncation and compensation techniques that prevent unauthorized model recovery while maintaining performance for authorized users.
Foundation modelsandlow-rank adaptersenable efficient on-device generative AI but raise risks such as intellectual property leakage andmodel recovery attacks. Existing defenses are often impractical because they require retraining or access to the original dataset. We propose LoREnc, a training-free framework that secures both FMs and adapters viaspectral truncationandcompensation. LoREnc suppresses dominant low-rank components of FM weights, compensates for the missing information in authorized adapters, and further appliesorthogonal reparameterizationto obscure structural fingerprints of the protected adapter. Unauthorized users produce structurally collapsed outputs, while authorized users recover exact performance. Experiments demonstrate that LoREnc provides strong protection against model recovery with under 1%computational overhead.
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edited about 1 hour ago LoREnc protects foundation model weights from extraction by injecting noise into the FM that cancels out in the LoRA adapter — the fine-tuned output stays intact while FM weights remain obfuscated. Accepted at ICIP 2026.
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