Researchers discovered that language models like GPT-3.5 Turbo could inadvertently reveal their training data when prompted to repeat simple words, leaking sensitive content, personal information, and copyrighted material. The technique, known as a divergence attack, had a success rate of 3% and poses a significant security risk. Companies have been notified, with the web version of ChatGPT potentially already patched.
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Unlocking the Potential of AI for Middle Managers
Recent Findings: Google Deep Mind and university researchers have discovered that Large Language Models (LLMs) like GPT-3.5 Turbo can inadvertently reveal their training data. This is significant because it includes sensitive information that should remain confidential.
How Does This Happen?
A simple method known as a “divergence attack” can cause LLMs to output information directly from their training datasets. For example, by prompting ChatGPT to endlessly repeat a word, it may start to disclose actual data it was trained on, such as personal contact information or copyrighted text.
Practical Implications: This vulnerability, while only successful about 3% of the time, poses a risk of data exposure. It indicates the need for careful management and monitoring of AI models to protect sensitive information.
Proactive Measures:
The companies responsible for these AI models have been informed, and it appears fixes have been implemented, particularly for the web version of ChatGPT.
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