  Language models (LMs) are susceptible to "memorizing" training data,
including a large amount of private or copyright-protected content. To
safeguard the right to be forgotten (RTBF), machine unlearning has emerged as a
promising method for LMs to efficiently "forget" sensitive training content and
mitigate knowledge leakage risks. However, despite its good intentions, could
the unlearning mechanism be counterproductive? In this paper, we propose the
Textual Unlearning Leakage Attack (TULA), where an adversary can infer
information about the unlearned data only by accessing the models before and
after unlearning. Furthermore, we present variants of TULA in both black-box
and white-box scenarios. Through various experimental results, we critically
demonstrate that machine unlearning amplifies the risk of knowledge leakage
from LMs. Specifically, TULA can increase an adversary's ability to infer
membership information about the unlearned data by more than 20% in black-box
scenario. Moreover, TULA can even reconstruct the unlearned data directly with
more than 60% accuracy with white-box access. Our work is the first to reveal
that machine unlearning in LMs can inversely create greater knowledge risks and
inspire the development of more secure unlearning mechanisms.
