Gemini Cloning Attempt: What Exactly Happened?
Google has revealed that its flagship AI chatbot, Gemini, was targeted by “commercially motivated” actors trying to clone its capabilities. In at least one campaign, attackers sent more than 100,000 prompts to Gemini, repeatedly querying the system to map out how it thinks and responds.
The company says these attacks are a form of “distillation” or “model extraction,” in which outsiders attempt to reverse‑engineer a model’s behavior by studying its answers at scale. Their goal is to replicate Gemini’s patterns and logic so they can build or improve their own AI systems without investing the same time and money.
2. What Is A Distillation Or Model Extraction Attack?
In a distillation attack, adversaries bombard an AI model with carefully designed questions and record its responses. Over time, they use those answers to train a new model that imitates the original system’s style, reasoning and decision‑making.
Google describes this behavior as a direct threat to its intellectual property, because large language models like Gemini represent years of research and billions of dollars in investment. Even if attackers never see the original training data, a close copy of the model’s behavior can still be extremely valuable and harmful to the company that built it.
3. Who Is Trying To Clone Gemini – And Why?
According to Google, most of the attempts to clone Gemini appear to come from private companies and researchers looking for a competitive edge in the AI race. A spokesperson said the attacks seem to originate from around the world but did not publicly identify specific groups or countries.
John Hultquist, chief analyst at Google’s Threat Intelligence Group, warned that Gemini is likely just the beginning. He argued that once attackers refine these methods on a big target like Google, they will start using the same techniques against smaller companies and custom AI tools trained on sensitive data.
4. Why AI Models Are So Hard To Protect
Tech companies design their chatbots to be open and easy to use, which makes them inherently vulnerable to this kind of probing. Anyone with an internet connection can send thousands of queries and slowly learn how the system behaves.
Google says it has mechanisms in place to detect and block distillation attacks, and it updated Gemini’s defenses after spotting the 100,000‑prompt campaign. Still, the company admits that as long as powerful models are publicly accessible, there is always some risk that determined actors will try to copy them.
5. How Other AI Companies Have Been Targeted
Google is not alone in facing model extraction attempts. OpenAI, the company behind ChatGPT, has previously accused Chinese rival DeepSeek of using distillation attacks to improve its own AI models.
These high‑profile disputes show that copying a competitor’s model has become a major front in the global AI competition. Instead of stealing raw data or source code, attackers try to siphon off the “knowledge” of a model by learning from its responses at scale.
6. Why This Matters For Businesses Using Custom AI
Google warns that the same techniques used against Gemini could be turned on smaller, specialized models trained on highly sensitive information. For example, a financial firm might build its own LLM based on decades of proprietary trading strategies, or a hospital system might train a model on confidential medical data.
If attackers can repeatedly prompt those models and distill their behavior, they may be able to extract valuable patterns, insights or strategies without ever breaching the underlying databases. Hultquist said that in theory, “you could distill” part of a company’s secret methods simply by interacting with its AI tools.
7. The Intellectual Property And Security Risks
From Google’s perspective, large‑scale distillation campaigns are a form of intellectual property theft. Companies see their top models as trade secrets, and losing control of those capabilities could undermine both their business and their competitive position.
There are also broader security concerns. If attackers can cheaply clone powerful models, they may use them to generate more convincing scams, create targeted misinformation or build tools that help with cyberattacks and surveillance. That risk grows as cloned models become more accurate copies of the originals.
8. What Google Is Doing To Protect Gemini
In its report, Google says it has strengthened Gemini’s defenses to better detect and block distillation attacks. That includes monitoring for unusual patterns of prompts, such as extremely high‑volume or highly structured queries that look more like automated scraping than normal user behavior.
The company did not reveal exact technical details, but it emphasized that protecting the model’s proprietary “reasoning” abilities is now a key part of its security strategy. Google also frames Gemini as a kind of early warning system: a large, visible target that reveals tactics attackers may later use against many other AI services.
9. What This Means For The Future Of AI Security
The incident around Gemini shows that AI security is no longer just about blocking hackers from stealing data or breaking into servers. Defenders now have to protect the behavior of the models themselves from being copied through clever questioning.
For businesses, this means treating custom models like any other critical asset: limiting open access, monitoring usage patterns, and building safeguards against automated probing. As more organizations rely on AI to handle sensitive tasks, defending against distillation and model extraction attacks will become a central part of cybersecurity strategy.
10. Final Thoughts: Innovation vs. Protection
Google’s experience with 100,000‑plus prompts aimed at cloning Gemini highlights the tension at the heart of modern AI development. Companies want their chatbots to be widely available and useful, but that openness also creates new ways for rivals and attackers to copy their work.
Balancing accessibility, innovation and protection will be one of the biggest challenges for the AI industry in the coming years. The Gemini cloning attempt is likely a preview of the pressures that every major AI platform—and many smaller ones—will face as the technology spreads.
