What Remains After Concept Erasure? Sparse Autoencoder Probes for Auditing Diffusion Model

Concept erasure aims to remove selected visual concepts from text-to-image diffusion models while preserving the generation of non-target concepts. Evaluation typically measures target suppression and non-target preservation in generated images, but a concept's absence from these outputs does not establish its removal from the model. We introduce SAE-Audit, a white-box audit using sparse autoencoder (SAE) probes to test whether activation interventions can restore target-concept generation without changing model weights or test prompts. After verifying target suppression, we compare the original and edited models using a shared SAE representation learned from original-model activations, identifying features whose target-related responses weaken after erasure. We inject sparse directions derived from the original-model responses into the edited model, keeping these directions fixed across test prompts, and evaluate recovery on held-out scene families. Experiments span six erasure methods and four domains: objects, artistic styles, copyrighted content, and nudity. Interventions using a few dozen selected SAE features yield higher average recovery rates than matched random-feature interventions, with recovery concentrated in objects and nudity following cross-attention edits. Most styles and copyrighted concepts show limited recovery under the tested intervention settings. These findings show that output suppression can coexist with responsiveness to sparse concept directions supplied from the original model.