Prompt-Based Safety Guidance Is Ineffective for Unlearned Text-to-Image Diffusion Models

Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address this issue, two main approaches have emerged: (1) fine-tuning the model to unlearn harmful concepts and (2) training-free guidance methods that leverage negative prompts. However, we observe that combining these two orthogonal approaches often leads to marginal or even degraded defense performance. This observation indicates a critical incompatibility between two paradigms, which hinders their combined effectiveness. In this work, we address this issue by proposing a conceptually simple yet experimentally robust method: replacing the negative prompts used in training-free methods with implicit negative embeddings obtained through concept inversion. Our method requires no modification to either approach and can be easily integrated into existing pipelines. We experimentally validate its effectiveness on nudity and violence benchmarks, demonstrating consistent improvements in defense success rate while preserving the core semantics of input prompts.

Key Contributions

• Identifies a critical incompatibility between concept unlearning (training-based) and training-free negative prompt guidance methods that causes marginal or degraded defense performance when combined
• Proposes replacing explicit negative text prompts with implicit concept embeddings recovered via diffusion-based concept inversion, bridging the two safety paradigms
• Demonstrates consistent improvement in defense success rate on nudity and violence benchmarks with no modification to the underlying unlearning or guidance methods

🛡️ Threat Analysis

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