Optimizing Content Policies against Unauthorized Use by Large Language Models

Large language models (LLMs) are increasingly relying on content provided by users or crawled from external sources to carry out tasks. However, source owners may share or publish material without authorizing their contents for use by LLMs. We propose **Policy Adaptation for Content Protection (PACT)**, a method for optimizing content-use policies that enable source owners to unilaterally restrict such unwanted LLM use. PACT formulates content-use restriction as policy induction over the hierarchical policy structure that connects protection premises to response directives governing the model's response. Within this formulation, premise-conditioned generation constructs the response directives, while model-guided evolutionary search uses response feedback to jointly refine the premise and directive structure. The search adjusts how the restriction is expressed while keeping the protected content and external task unchanged. We evaluate the resulting policies on question answering and summarization across four sensitive content domains and five models. On unseen content, the policies achieve 91.78%-100% answer suppression under Global Protection. The policies also support targeted protection and generalize across models, with policies optimized using GPT-5.6-Sol exceeding 80% suppression on all four other models under Global Protection. These findings suggest that content-use policies can help source owners limit unwanted LLM use, while distinguishing generalization to new content from transfer to another model.