Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence. (arXiv:2305.03010v1 [cs.CL])

Sentence-level representations are beneficial for various natural language
processing tasks. It is commonly believed that vector representations can
capture rich linguistic properties. Currently, large language models (LMs)
achieve state-of-the-art performance on sentence embedding. However, some
recent works suggest that vector representations from LMs can cause information
leakage. In this work, we further investigate the information leakage issue and
propose a generative embedding inversion attack (GEIA) that aims to reconstruct
input sequences based only on their sentence embeddings. Given the black-box …

art attack capture expect generative information language language models large leaks linguistic lms natural language natural language processing performance recover state