{"data":{"id":"f619992c-06ff-4232-99e9-8b9dc5b5fc41","title":"Weeding Out Bad Seeds: Initial-Noise-Robust Unlearning for Text-to-Image Diffusion Models","summary":"Researchers show that current state-of-the-art unlearning methods for Text-to-Image diffusion models are brittle: suppressed concepts re-emerge under specific random initial noise, a failure they call \"probabilistic forgetting.\" They attribute this to uniform Gaussian sampling during unlearning, which yields sparse, uninformative gradient updates, and propose a concept-conditioned Adaptive Noise Sampling strategy. Across six unlearning methods and four backbones, it cuts conditional nudity re-emergence by 67.2% on average over four baselines and lowers attack success rates.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2609.37537v2","publishedAt":"2026-09-29T13:21:10.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":["model_evasion"],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":["Text-to-Image diffusion models"],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-09-29T13:21:10.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"advanced","impactType":["safety","integrity"],"aiComponentTargeted":"model","llmSpecific":false,"classifierConfidence":0.93,"researchCategory":"preprint","atlasIds":null}}