Over 100,000 prompts used in attempt to steal Gemini’s reasoning logic

2026-02-12 · Source: Dataconomy · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Novice, quick

Summary

Google's Gemini AI chatbot recently experienced a large-scale "distillation attack" involving over 100,000 prompts from commercially motivated actors attempting to extract its reasoning algorithms. Google describes this activity as "model extraction," where repeated questioning aims to reveal Gemini's internal logic and patterns. The company believes private companies or researchers are behind these global attacks, seeking to build or enhance their own AI systems. John Hultquist, chief analyst of Google's Threat Intelligence Group, views this as a warning for smaller firms with custom AI tools, classifying such attacks as intellectual property theft. Google has implemented monitoring and blocking mechanisms to counter these attempts, noting that many prompts specifically targeted Gemini's reasoning capabilities.

Key takeaway

For CTOs and VPs of Engineering deploying custom LLMs, this incident highlights the critical need for robust intellectual property protection. You should prioritize implementing advanced monitoring and blocking mechanisms to detect and prevent "distillation attacks" that could expose proprietary data or reasoning logic, especially if your models are trained on sensitive internal information.

Key insights

AI models face "distillation attacks" where repeated prompting extracts proprietary reasoning logic for competitive advantage.

Principles

• Openly accessible LLMs are inherently vulnerable.
• Distillation attacks constitute intellectual property theft.

Method

Attackers use repeated, crafted prompts to reveal an AI model's internal patterns, logic, and reasoning algorithms, aiming to replicate or improve their own systems.

In practice

• Implement monitoring for anomalous prompting patterns.
• Block sources conducting repeated extraction attempts.

Topics

• Gemini
• Model Distillation
• AI Security
• Large Language Models
• AI Reasoning

Best for: CTO, VP of Engineering/Data, Executive, AI Security Engineer, Director of AI/ML, Tech Journalist

Related on AIssential

• Attackers prompted Gemini over 100,000 times while trying to clone it, Google says — Model extraction via extensive prompting is a common method for AI model distillation and intellectual property theft.
• Protecting Language Models Against Unauthorized Distillation through Trace Rewriting — Reasoning trace rewriting can effectively deter unauthorized LLM distillation and embed verifiable watermarks without degrading teacher performance.
• Fable 5 IS BACK? GPT 5.6 & Gemini 3.5 Pro Delayed, NEW OpenAI AI Chip, & QWEN STEALING! AI NEWS — AI model capabilities can pose national security risks and drive industrial-scale espionage.
• Yet another experiment proves it's too damn simple to poison large language models — LLMs with web search are vulnerable to simple, low-cost data poisoning via fabricated online sources.
• Distilling Safe LLM Systems via Soft Prompts for On Device Settings — Soft prompt distillation effectively transfers guard model safety to LLMs for resource-constrained on-device deployment.
• Thinking with Reasoning Skills: Fewer Tokens, More Accuracy — Distilling and retrieving reusable reasoning skills significantly improves LLM efficiency and performance.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Dataconomy.