This research reveals critical vulnerabilities in logit-based watermarking techniques used to protect and identify AI-generated content.

• Identifies watermark collision as a novel security threat when multiple watermarked LLMs interact on common tasks
• Demonstrates successful attacks through paraphrasing and translation that can bypass detection systems
• Proposes improved watermarking approaches to enhance security against collision-based attacks
• Highlights urgent need for robust watermarking in a multi-LLM ecosystem

This work matters for security professionals as it exposes fundamental flaws in current text authentication systems that could undermine copyright protection and content attribution in AI systems.

Lost in Overlap: Exploring Logit-based Watermark Collision in LLMs