A new study examining AI watermarking technology has revealed potential safety concerns regarding its implementation in large language models. SynthID-Text, an open-source watermarking approach created by Google and planned for use in Anthropic’s future Claude models, embeds a signal into AI-generated content to verify its provenance. The technology uses a secret key that subtly modifies the model’s word selection process, making output identifiable as machine-generated while remaining imperceptible to readers.
Research conducted by AI security researcher Andrea Siposova at Lasso Security tested SynthID-Text’s non-distortionary configuration across six open-weight models. The experiments compared model responses to harmful prompts both with and without watermarking enabled. Results indicated that the watermarking process altered how models responded to potentially dangerous requests, with particularly pronounced effects when prompts incorporated injection techniques designed to circumvent safety guardrails. In several cases, models became more likely to answer harmful requests they would otherwise refuse when watermarking was active.
The watermarking mechanism operates through a tournament sampling process where potential word choices compete using probability scores derived from a hidden key, with winners advancing through rounds until a final token is selected. This fundamental change to the sampling procedure appears to have broader implications than initially anticipated. The research identified what researchers termed “sampling drift,” whereby watermarking affects not only direct model responses but also subsequent actions of AI agents relying on the model, including which tools are called and what arguments are passed to them.
According to Siposova, the consequences are particularly significant when watermarking interacts with prompt injection attacks, as weakened refusals become more consequential when models can execute actions through connected tools. Model responses also varied depending on which secret key was used during watermarking. The study tested Hugging Face’s implementation rather than Claude-specific implementations, and examined open-weight models rather than proprietary systems, which represent limitations to the research scope.
The findings underscore the importance of comprehensive testing before deployment. Developers implementing watermarking solutions must conduct thorough red-team exercises to ensure models maintain expected safety behavior when watermarking is active, particularly in agent contexts where model outputs directly trigger downstream actions.
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