少点错误 2024年07月22日
Using an LLM perplexity filter to detect weight exfiltration
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文章探讨了如何通过使用小语言模型进行复杂度过滤,来提高大型模型权重泄露的难度。提出的方法是通过计算输出数据的复杂度,区分正常模型输出和试图以文本形式泄露的模型权重,以此增强数据中心的模型安全。

🔍 文章首先指出,当前大型模型的参数量可达数万亿,文件大小达到数TB。Ryan Greenblatt提出的数据中心模型生命周期内总流出数据量与模型大小相当的观点,为增强模型保护提供了思路。

🎛️ 复杂度过滤的核心思想是利用小语言模型对推断服务器所有输出数据进行复杂度计算。正常语言输出复杂度低,而编码的模型权重复杂度高,因此可以通过复杂度过滤来识别并阻止泄露尝试。

🛡️ 该方法的优势在于:大型模型权重的编码难以伪装成自然语言,具有压缩抵抗性;运行小语言模型作为过滤器的计算成本相对较低;并且该方法与上传限制相辅相成,提高了数据中心的模型安全。

🔐 文章还讨论了该方法的可行性,认为如果流出数据量与模型大小相差几个数量级,这种复杂度过滤技术可能使模型权重在退役前几乎无法被泄露,前提是数据中心具有出色的物理安全措施。

Published on July 21, 2024 6:18 PM GMT

Recently, there has been discussion on how to make model weight exfiltration more difficult, such as Ryan Greenblatt's proposal for upload limits. A key factor enabling this is that the critical data we want to protect (model weights) are very large files. Current models can have trillions of parameters, which translates to terabytes of data. Ryan calculated that the total amount of outgoing data from a data center during a model's lifetime could be within a few orders of magnitude of the size of the model itself. This post proposes an additional layer of security that could significantly enhance the effectiveness of upload limits: perplexity filtering using a small language model.

Perplexity, in the context of language models, is a measure of how surprised a model is by a given sequence of text. More formally, it's the exponential of the cross-entropy of the text under the model. In simpler terms, if a piece of text has low perplexity, it means the model finds it very predictable or natural; high perplexity indicates that the text is unexpected or unnatural to the model. By using perplexity as a filter, we can potentially distinguish between normal model outputs (which should have relatively low perplexity) and attempts to exfiltrate model weights encoded as text (which would likely have high perplexity).

The core idea is to use a small language model as a perplexity filter on all outgoing data from the inference servers. Here's how it would work:

All outgoing data is treated as language tokens. A small language model (much smaller than the main model being protected) is used to calculate the perplexity of these tokens. Data with high perplexity (i.e., data that doesn't look like natural language to the small model) is blocked.

Why This Could Work

If the amount of outgoing data is within a few orders of magnitude of the model size itself, it's possible that a few tricks like this could make it almost impossible to exfiltrate model weights before the model is deprecated (assuming that the data center has excellent physical security).



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模型安全 复杂度过滤 小语言模型 数据泄露防护
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