arXiv:2511.04711v2 Announce Type: replace-cross Abstract: Large-scale vision-language models, especially CLIP, have demonstrated remarkable performance across diverse downstream tasks. Soft prompts, as carefully crafted modules that efficiently adapt vision-language models to specific tasks, necessitate effective copyright protection. In this paper, we investigate model copyright protection by auditing whether suspicious third-party models incorporate protected soft prompts. While this can be viewed as a special case of model ownership auditing, our analysis shows that existing techniques are
The proliferation of advanced AI models and the increasing commercial value of their components necessitate robust intellectual property protection mechanisms.
Protecting 'soft prompts' in large language models is critical for incentivizing innovation and defining ownership in the rapidly evolving AI intellectual property landscape.
The ability to audit and claim copyright on specific AI model components like soft prompts could reshape business models and collaborations within the AI development ecosystem.
- · AI model developers
- · Intellectual property lawyers
- · Companies with proprietary prompt engineering
- · Pirates and unauthorized users of AI models
- · Entities engaged in intellectual property infringement
- · Open-source AI projects without clear licensing
Increased focus on intellectual property protection for AI-specific components like soft prompts.
Development of new legal frameworks and technical standards for AI copyright auditing and enforcement.
Consolidation in the AI industry as companies with strong IP portfolios gain competitive advantage, or conversely, a fragmentation as new IP structures enable novel forms of collaboration and competition.
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Read at arXiv cs.LG