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Preemptive LLM Unlearning against Forbidden Capability Acquisition via Gradient Sealing
Open-weight LLMs are released not only as fixed products but also as substrates for downstream fine-tuning. This openness, however, creates legal and ethical risks because users may misuse fine-tuning to instill illicit knowledge or enable hostile operations. Model providers therefore need apre-rele
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