LoRAShield: Data-Free Editing Alignment for Secure Personalized LoRA Sharing
Journal:
arXiv
Published Date:
Jul 5, 2025
Abstract
The proliferation of Low-Rank Adaptation (LoRA) models 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 risks: benign
LoRAs can be weaponized by adversaries to generate harmful content (e.g.,
political, defamatory imagery), undermining creator rights and platform safety.
Existing defenses like concept-erasure methods focus on full diffusion models
(DMs), neglecting LoRA's unique role as a modular adapter and its vulnerability
to adversarial prompt engineering. To bridge this gap, we propose LoRAShield,
the first data-free editing framework for securing LoRA models against misuse.
Our platform-driven 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.
