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

TL;DR

LoRAShield is a novel data-free framework that dynamically edits LoRA models to prevent malicious content generation, enhancing security and trustworthiness in personalized text-to-image generation sharing platforms.

Contribution

It introduces the first data-free editing method for LoRA models, addressing their unique vulnerabilities and enabling secure sharing without compromising benign functionalities.

Findings

Effectively blocks malicious content generation

Maintains model performance on benign tasks

Operates efficiently without additional data

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.