研究者近乎完美地从输出文本逆向还原LLM提示词
Researchers can now reverse-engineer LLM prompts from output text with near-perfect accuracy
Researchers at IIT Bombay and Adobe Research have built an inverse language model that reconstructs the original prompt from an LLM's output with near-perfect accuracy. Their method, called "Previous-Token Prediction," doesn't need access to model weights and works across different models. For companies relying on proprietary system prompts, this could be a serious security risk.
IIT Bombay和Adobe Research的研究人员构建了一个逆向语言模型，能够以近乎完美的准确度从LLM的输出中重建原始提示。他们的方法称为“前一个词元预测”，无需访问模型权重，且适用于不同模型。对于依赖专有系统提示的公司来说，这可能是一个严重的安全风险。
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文章《研究人员现在可以以近乎完美的准确度从输出文本中逆向工程LLM提示》首次出现在The Decoder上。
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