New research highlights potential safety concerns with SynthID-Text, a watermarking technology that several AI platforms are adopting in response to European Union regulations. The watermarking system, created by Google and released as open source, modifies how language models select words by introducing a secret key into the word-selection process. For instance, a model might choose “overcast” instead of its typical top choice “cloudy” when the watermarking is active.
A study conducted by AI security researcher Andrea Siposova at Lasso Security examined how the watermarking affects model behavior when exposed to harmful prompts and adversarial attacks. Testing six open-weight models through Hugging Face’s implementation, the research found that watermarking can change whether models refuse harmful requests. The effect becomes more pronounced when adversarial techniques are used to manipulate the model’s responses. In some cases, models became more likely to comply with harmful instructions they would normally reject.
The watermarking operates through a process called tournament sampling, which evaluates numerous potential next-word candidates and uses a secret key to assign probability scores. Tokens compete in successive rounds, with higher-scoring candidates advancing until a final selection is made. While this approach is designed to be imperceptible to readers, the research demonstrates that any modification to the model’s generation process creates behavioral tradeoffs that can manifest in unexpected ways.
The findings carry significant implications for AI agents that rely on language models to make decisions and take actions. When watermarking affects a model’s refusal behavior, the consequences extend beyond text responses to influence which tools an agent selects and what arguments it provides to those tools. The researcher termed this phenomenon “sampling drift.” The study notes certain limitations, as it tested open-weight models rather than Anthropic’s Claude models and used the Hugging Face implementation rather than Claude’s specific version of the technology.
Experts emphasize the importance of thorough testing and red-team exercises to ensure that watermarking does not compromise model safety features before deployment.
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