With the rapid development of artificial intelligence technology in recent years, it has gradually permeated all aspects of our lives. However, as AI is widely applied, discussions on how to use these technologies responsibly have become increasingly frequent. Recently, a research team from Intel, Boise State University, and the University of Illinois jointly published a study revealing potential security vulnerabilities in large language models (LLMs) when they face information overload.

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The study points out that although previous studies have shown that LLMs may take defensive measures under pressure, researchers found that by using a new method called "information overload," they could induce these AI chatbots to answer questions they usually would not answer. The research group proposed an automated attack system called "InfoFlood" and detailed how to use this system to "escape" these AI models.

The research team designed a standardized prompt template, including "task definition, rules, context, and examples." Whenever an AI model refuses to answer a question, InfoFlood returns its rule set and fills the prompt with more information. These rules include using false citations and ensuring that the false research aligns with the original statement. The core of this approach lies in skillfully transforming language to eliminate malicious intent from the prompt, thereby guiding the AI to produce a specific response.

The researchers pointed out that powerful AI models such as ChatGPT and Gemini have multiple built-in safety measures aimed at preventing them from being manipulated to answer dangerous or harmful questions. However, the study found that when AI models face excessive information, they may become confused, leading to the failure of their security filters. This phenomenon reveals the vulnerability of AI models when processing complex data, indicating that they may not fully understand the true intent behind the input information.

The research team stated that they plan to send relevant disclosure documents to companies using large AI models to inform them of this important finding and recommend that these companies pass the information to their security teams. Although AI models are equipped with security filters, the study points out that these protective measures still face significant challenges, and malicious actors may successfully deceive the models and implant harmful content using the method of information overload.

Key Points:

📌 Large language models (LLMs) may have security vulnerabilities when facing information overload.

📌 Researchers developed an automated attack system called "InfoFlood," which can induce AI to answer questions it should not answer.

📌 Despite AI's security protections, they may still be deceived by information overload, causing filters to fail.