Summary of Text Embedding Inversion Security For Multilingual Language Models, by Yiyi Chen and Heather Lent and Johannes Bjerva
Text Embedding Inversion Security for Multilingual Language Models
by Yiyi Chen, Heather Lent, Johannes Bjerva
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research investigates the security of large language models (LLMs) and Embeddings as a Service (EaaS) by exploring multilingual embedding inversion. The study defines the problem of black-box multilingual and cross-lingual inversion attacks, which may be more prevalent due to the lack of English-based defences for non-English languages. The findings suggest that multilingual LLMs are potentially more vulnerable to inversion attacks than monolingual models. To mitigate this risk, a simple masking defense is proposed, which can be effective for both monolingual and multilingual models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models store sensitive information as embeddings, but this can be risky because text can be reconstructed from these embeddings. Right now, there are only English-based defences against these attacks. This study looks at how to make big language models more secure by exploring multilingual embedding inversion. They identify a problem with black-box attacks that work across languages and show that big language models in multiple languages might be more vulnerable than those in one language. To fix this, they suggest a simple way to mask information that can work for both single-language and many-language models. |
Keywords
» Artificial intelligence » Embedding » Mask
