Distillation

Attackers prompted Gemini over 100,000 times while trying to clone it, Google says

AI, AI language models, AI security, Biz & IT, chatgpt, chatgtp, DeepSeek, Distillation, Gemini, Google, Google Gemini, large language models, LLMs, machine learning, openai / Shannon Garcia / February 12, 2026

On Thursday, Google announced that “commercially motivated” actors have attempted to clone knowledge from its Gemini AI chatbot by simply prompting it. One adversarial session reportedly prompted the model more than 100,000 times across various non-English languages, collecting responses ostensibly to train a cheaper copycat.

Google published the findings in what amounts to a quarterly self-assessment of threats to its own products that frames the company as the victim and the hero, which is not unusual in these self-authored assessments. Google calls the illicit activity “model extraction” and considers it intellectual property theft, which is a somewhat loaded position, given that Google’s LLM was built from materials scraped from the Internet without permission.

Google is also no stranger to the copycat practice. In 2023, The Information reported that Google’s Bard team had been accused of using ChatGPT outputs from ShareGPT, a public site where users share chatbot conversations, to help train its own chatbot. Senior Google AI researcher Jacob Devlin, who created the influential BERT language model, warned leadership that this violated OpenAI’s terms of service, then resigned and joined OpenAI. Google denied the claim but reportedly stopped using the data.

Even so, Google’s terms of service forbid people from extracting data from its AI models this way, and the report is a window into the world of somewhat shady AI model-cloning tactics. The company believes the culprits are mostly private companies and researchers looking for a competitive edge, and said the attacks have come from around the world. Google declined to name suspects.

The deal with distillation

Typically, the industry calls this practice of training a new model on a previous model’s outputs “distillation,” and it works like this: If you want to build your own large language model (LLM) but lack the billions of dollars and years of work that Google spent training Gemini, you can use a previously trained LLM as a shortcut.

Attackers prompted Gemini over 100,000 times while trying to clone it, Google says Read More »

AI firms follow DeepSeek’s lead, create cheaper models with “distillation”

Thanks to distillation, developers and businesses can access these models’ capabilities at a fraction of the price, allowing app developers to run AI models quickly on devices such as laptops and smartphones.

Developers can use OpenAI’s platform for distillation, learning from the large language models that underpin products like ChatGPT. OpenAI’s largest backer, Microsoft, used GPT-4 to distill its small language family of models Phi as part of a commercial partnership after investing nearly $14 billion into the company.

However, the San Francisco-based start-up has said it believes DeepSeek distilled OpenAI’s models to train its competitor, a move that would be against its terms of service. DeepSeek has not commented on the claims.

While distillation can be used to create high-performing models, experts add they are more limited.

“Distillation presents an interesting trade-off; if you make the models smaller, you inevitably reduce their capability,” said Ahmed Awadallah of Microsoft Research, who said a distilled model can be designed to be very good at summarising emails, for example, “but it really would not be good at anything else.”

David Cox, vice-president for AI models at IBM Research, said most businesses do not need a massive model to run their products, and distilled ones are powerful enough for purposes such as customer service chatbots or running on smaller devices like phones.

“Any time you can [make it less expensive] and it gives you the right performance you want, there is very little reason not to do it,” he added.

That presents a challenge to many of the business models of leading AI firms. Even if developers use distilled models from companies like OpenAI, they cost far less to run, are less expensive to create, and, therefore, generate less revenue. Model-makers like OpenAI often charge less for the use of distilled models as they require less computational load.