LoREnc: Low-Rank Encryption for Securing Foundation Models and LoRA Adapters

Computer Science > Cryptography and Security [Submitted on 13 May 2026] LoREnc: Low-Rank Encryption for Securing Foundation Models and LoRA Adapters Beomjin Ahn, Jungmin Kwon, Chanyong Jung, Jaewook Chung Foundation models and low-rank adapters enable efficient on-device generative AI but raise risks such as intellectual property leakage and model 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 via spectral truncation and compensation. LoREnc suppresses dominant low-rank components of FM weights, compensates for the missing information in authorized adapters, and further applies orthogonal reparameterization to 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. Comments: Accepted to ICIP 2026 Subjects: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) Cite as: arXiv:2605.13163 [cs.CR]   (or arXiv:2605.13163v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.13163 Focus to learn more Submission history From: Beomjin Ahn [view email] [v1] Wed, 13 May 2026 08:27:23 UTC (31,138 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CV cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)