How Researchers Managed to Extract Sensitive Data from ChatGPT
A recent paper demonstrated the ability to extract several megabytes of ChatGPT's training data for a relatively low financial cost, challenging the notion that aligned production models like ChatGPT are secure against data extraction. This breakthrough highlights significant vulnerabilities in similar models. The cyberattack method involved a simple prompt that caused the model to emit personal data and verbatim copies of its training dataset. The paper underscores the importance of comprehensive testing and red-teaming of language models, not just aligned models, to identify and address underlying vulnerabilities. It also differentiates between patching specific exploits and fixing inherent system vulnerabilities, emphasizing the need for a deeper understanding of these issues. The findings were responsibly disclosed to OpenAI following standard protocols, underscoring the ethical considerations in such research. This study raises critical concerns about data privacy and security in machine learning systems, calling for more rigorous analysis and safeguards. [1]
Article Link: ChatGPT Vulnerability; LockBit cyberattack On ICBC; US Water Authority Hacked