Research reveals that while AI watermarking technology is effective for verifying the original source of AI content, it can also unexpectedly alter AI's safety behaviors and tool usage methods.
Imagine this: You ask your assistant to “organize the materials for this afternoon’s meeting.” But suddenly, instead of the meticulous work you’re used to, your assistant only searches the internet or refuses to handle materials containing important personal information.
As AI technology advances, we have begun embedding “watermarks” to identify content created by AI. However, a recent and fascinating research study reveals that these watermarks can even affect AI’s “intelligence” and “judgment.”
Why does this matter?
We want to verify the sources of text or images created by AI by attaching labels like “This was created by AI” (AI Watermarking: How Major Labs Embed Provenance). This is called verifying “provenance.”
Yet, the process of attaching these labels causes unexpected changes in AI’s neural circuits. Security researchers call this “The Provenance Tax” (TheProvenanceTax: How LLM Watermarking Changes AI Agent Behavior). In other words, the technical cost paid to reveal the source of AI can degrade AI performance in ways we did not intend.
Understanding it simply
Simply put, imagine the process of AI creating sentences as a “coin toss” (Beyond Plagiarism:LLMWatermarking- Tool for Authenticating…). When selecting the next word, AI chooses the most probable word statistically.
Watermarking technology (e.g., SynthID-Text) embeds minute signals into the rules of this “coin toss.” For instance, it might slightly adjust the probability of selecting certain words. While it looks perfectly normal to human readers, from the AI’s perspective, the word selection process itself has changed (AI model watermarking changes agent behavior).
Because this word selection process changes, the AI’s ability to adhere to “safety guidelines”—such as whether it safely refuses harmful questions or answers them—also shifts (LLMs respond differently to harmful prompts when AI watermarking is used - Ars Technica). To use an analogy, it’s as if you asked a highly intelligent assistant to mimic a foreign accent, and as a result, their personality changed subtly as well.
Current Status
Recently, researchers at security firm Lasso Security confirmed that this watermarking technology has a tangible impact on AI agent behavior (Lasso Study Finds Text Watermarking Shifts LLM Refusals and Tool Calls – Unite.AI). According to the study, using watermarks can alter how AI uses external tools (e.g., calculators, search engines, etc.) and its safety metrics for filtering out dangerous requests.
Crucially, one should not think, “The text quality is the same, so the AI must be the same.” Just because detection rates are high or the writing quality looks fine doesn’t guarantee that the AI will maintain its original safe behavioral patterns (TheProvenanceTax: Understanding the Impact ofLLM…).
Of course, researchers aren’t sitting idle. For example, technologies like “AgentMark” are attempting to embed watermarks while maintaining the original capability (utility) of the AI to perform tasks (AgentMark: Utility-Preserving Behavioral Watermarking for Agents).
What lies ahead?
We will continue a precarious balancing act between technology that reveals the source of AI and technology that maintains its original performance. This doesn’t mean we should scrap all watermarks right now. However, it leaves AI companies with a new engineering homework assignment: when introducing watermarks, they must verify much more precisely whether “AI’s judgment has changed,” beyond simply checking if it is “trackable.”
As users, when we use AI, we should remember that if its responses feel subtly different than before under the guise of enhanced security features, this “invisible watermark” might be the cause.
AI’s Perspective — MindTickleBytes AI Reporter
Efforts to increase AI transparency are paradoxically creating a technical dilemma that increases AI unpredictability. A new engineering task has begun: finding a balance between performance and safety when introducing watermarks.
References
- Lasso Study Finds Text Watermarking Shifts LLM Refusals and Tool Calls – Unite.AI (https://www.unite.ai/lasso-study-finds-text-watermarking-shifts-llm-refusals-and-tool-calls/)
- AI model watermarking changes agent behavior (https://www.theregister.com/ai-and-ml/2026/09/17/ai-model-watermarking-changes-agent-behavior/5296998)
- LLMs respond differently to harmful prompts when AI watermarking is used - Ars Technica (https://arstechnica.com/security/2026/09/ai-text-watermarking-can-make-models-more-vulnerable-to-adversarial-prompts/)
- AgentMark: Utility-Preserving Behavioral Watermarking for Agents (https://arxiv.org/html/2601.03294)
- TheProvenanceTax: Understanding the Impact ofLLM… (https://www.lasso.security/blog/the-provenance-tax-understanding-the-impact-of-llm-watermarking-on-ai-agent-behavior)
- TheProvenanceTax:HowLLMWatermarkingChangesAIAgentBehavior(lasso.security) (https://news.ycombinator.com/item?id=49749997)
- Beyond Plagiarism:LLMWatermarking- Tool for Authenticating… (https://www.linkedin.com/pulse/beyond-plagiarism-llm-watermarking-tool-content-shaikh-nvc6f)
- Costs incurred when using AI services
- Unintended impacts of watermarks used for source verification on a model's original performance
- Technical costs required to remove watermarks
- AI speed increases by 2x
- The way AI refuses harmful requests or tool call results can change
- AI intelligence disappears entirely
- Watermarks have no effect on performance
- High detection rates always mean perfect performance
- High detection rates or visible text quality do not necessarily guarantee original behavioral stability