Google's TIG says Gemini has been inundated by “commercially motivated” actors who are trying to clone it, including one campaign that prompted it 100K+ times
Google says private companies and researchers are trying to copy Gemini's capabilities by repeatedly prompting it at scale.
Context & Ripple Effects
Gemini’s exposure has expanded alongside reported growth in API activity and enterprise adoption, with internal data said to show a sharp rise in calls during 2025 and Gemini Enterprise reaching millions of subscribers. That scale makes separating normal customer use from systematic extraction more operationally consequential.
Google had already described Gemini as a productivity tool for APT groups across more than 20 countries, rather than a source of novel AI-enabled attacks. The newly reported cloning activity broadens the governance problem from misuse of model outputs to sustained attempts to replicate the model’s capabilities.
First-order effects
- Google must identify and disrupt high-volume prompting patterns that appear designed to collect outputs for model replication, while avoiding unnecessary friction for legitimate API and enterprise users.
- Actors seeking to clone Gemini face a less reliable route to gathering training or evaluation material if Google tightens detection, rate controls, or account enforcement.
Second-order effects
- Higher scrutiny of unusually intensive usage can raise compliance and access-management burdens for legitimate developers whose workflows resemble bulk querying.
- The report puts pressure on other frontier-model providers to treat output extraction as a distinct abuse category, alongside the productivity-focused use of Gemini by APT groups.
Third-order effects
- If repeated prompting becomes a common way to approximate proprietary models, model access will increasingly be governed as both a commercial distribution channel and a protected source of capabilities.
- The durable challenge is defining enforceable boundaries between ordinary model use, benchmarking, and extraction—an issue likely to shape API design and AI governance rather than one provider alone.
The trend: Frontier AI providers are moving toward tighter governance of model access as commercially motivated actors treat model outputs as inputs for competitive replication.