The exposure of large language models (LLMs) to copyrighted material during
pre-training raises concerns about unintentional copyright infringement post
deployment. This has driven the development of “copyright takedown” methods,
post-training approaches aimed at preventing models from generating content
substantially similar to copyrighted ones. While current mitigation approaches
are somewhat effective for average-case risks, we demonstrate that they
overlook worst-case copyright risks exhibits by the existence of long, verbatim
quotes from copyrighted sources. We propose BloomScrub, a remarkably simple yet
highly effective inference-time approach that provides certified copyright
takedown. Our method repeatedly interleaves quote detection with rewriting
techniques to transform potentially infringing segments. By leveraging
efficient data sketches (Bloom filters), our approach enables scalable
copyright screening even for large-scale real-world corpora. When quotes beyond
a length threshold cannot be removed, the system can abstain from responding,
offering certified risk reduction. Experimental results show that BloomScrub
reduces infringement risk, preserves utility, and accommodates different levels
of enforcement stringency with adaptive abstention. Our results suggest that
lightweight, inference-time methods can be surprisingly effective for copyright
prevention.
