AI Watermarking Vulnerabilities in Anthropic, Google, and OpenAI Models
AI model providers, specifically Anthropic, Google, and OpenAI, are deploying model-level watermarking—such as Google's SynthID-Text—to meet EU AI Act Article 50(2) transparency requirements. These systems embed signals by manipulating token probability distributions. However, research utilizing Linguistic Loop Formalism and Decay Laws ($\rho^{h+1}$) reveals these watermarks are highly susceptible to "semantic-preserving transformations." Techniques including machine translation and adversarial paraphrasing induce non-linear signal decay, enabling actors to strip provenance markers. This vulnerability transforms watermarking into a performative compliance measure rather than a robust security control, creating a false sense of authenticity and increasing the risk of undetected AI-generated misinformation.