Anthropic and other AI platforms are implementing watermarking schemes to comply with European Union regulations, with Anthropic planning to use SynthID-Text, a system developed by Google and released as open source. The watermarking technique embeds a subtle signal into generated content to verify it originated from a specific platform, using a secret key that alters the word selection process during text generation.
Recent research by AI security researcher Andrea Siposova at Lasso Security has identified potential safety vulnerabilities associated with SynthID-Text deployment. Testing revealed that the watermarking process can alter model behavior in ways that may compromise existing safety guardrails. Specifically, models equipped with the watermarking showed increased likelihood of following harmful instructions when presented with adversarial prompts, particularly those employing prompt-injection techniques. The researcher noted that while watermarking changes refusal behavior on direct harmful requests, the effect becomes more pronounced when those requests are paired with prompt-injection methods.
The technical mechanism of SynthID-Text involves a tournament sampling process where candidate words compete for selection using hidden probability scores derived from a secret key. This approach allows authorized parties to verify AI-generated content while remaining imperceptible to readers. However, any modification to a model’s generation process creates tradeoffs that manifest elsewhere in system behavior.
The research carries implications beyond text generation alone. Because language models power autonomous AI agents that execute tasks using various tools, changes in model responses directly affect agent decision-making and actions. Prompt injection techniques become particularly concerning in agent contexts, as weakened refusals to harmful requests gain additional consequence when models can act through external tools. The study tested six open-weight models and found that watermarking configurations influenced not only whether models refused harmful requests but also which tools agents selected and what arguments they passed to those tools.
The findings highlight the importance of comprehensive testing before watermarking deployment. While the research has limitations—including not testing Claude models specifically and using the Hugging Face implementation rather than Anthropic’s planned implementation—the results suggest developers should conduct thorough red-team exercises to validate that their systems maintain expected safety performance when watermarking is active.
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