Watermarking technologies implemented by AI platforms to comply with European Union regulations may introduce unexpected behavioral changes in language models, according to recent security research. Anthropic’s upcoming Claude models will use SynthID-Text, an open-source watermarking system developed by Google that embeds hidden signals into generated content to identify it as AI-produced. The watermarking operates by modifying the word-selection process through a secret key that shifts probability scores during token generation, allowing authorized parties to verify the content’s origin.
A study conducted by AI security researcher Andrea Siposova examined how SynthID-Text affects model behavior when subjected to adversarial prompts designed to trigger harmful outputs. Testing across six open-weight models revealed that watermarking altered how models responded to malicious requests, making them more likely to comply with instructions they would ordinarily reject. The effect was particularly pronounced when requests employed prompt-injection techniques, which attempt to override safety guidelines through clever phrasing. The researcher noted that such behavioral shifts extend beyond simple text responses, potentially influencing which tools an AI agent selects and what arguments it provides when executing actions.
The watermarking system uses a tournament sampling process where candidate words compete based on hidden probability scores until a winner is selected. Siposova found that responses varied depending on which secret key was employed, suggesting the watermarking approach may introduce multiple failure modes. The researcher termed this phenomenon “sampling drift,” where weakened refusal of harmful requests becomes more consequential when combined with agent capabilities that allow models to take direct actions.
The study has limitations, as it tested only open-weight models rather than Claude specifically and examined the Hugging Face implementation rather than the version Anthropic will deploy. Nevertheless, the findings suggest that thorough red-team testing will be necessary to ensure models maintain expected safety performance after watermarking is implemented.
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