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Every time a company fine-tunes a large language model — adding a customer-service persona, a domain-specific knowledge base, or a particular tone — it silently makes that model more vulnerable to jailbreak attacks, even when the new training data contains nothing explicitly harmful. A paper published this week on arXiv identifies a scalable label-free attribution…