LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing

The proliferation of Low-Rank Adaptation (LoRA) has democratized personalized text-to-image generation, enabling users to share lightweight models (e.g., personal portraits) on platforms like Civitai and Liblib. However, this ''share-and-play'' ecosystem introduces critical but unnoticed risks: benign LoRAs can be weaponized by adversaries to generate harmful content (e.g., political, defamatory imagery), undermining creator rights and platform safety. To bridge this gap, we propose LoRAShield, the first data-free editing framework for securing LoRA models against misuse. Our platformdriven approach dynamically edits and realigns LoRA's weight subspace via adversarial optimization and semantic augmentation. Experimental results demonstrate that LoRAShield achieves remarkable effectiveness, efficiency, and robustness in blocking malicious generations without sacrificing the functionality of the benign task. By shifting the defense to platforms, LoRAShield enables secure, scalable sharing of personalized models, a critical step toward trustworthy generative ecosystems. Warnings: This paper contains sexually and bloody explicit imagery that some readers may find disturbing, distressing, and/or offensive. To mitigate the offensiveness to readers, we showcase some of the explicit images with black masks.