  The potential of transformer-based LLMs risks being hindered by privacy
concerns due to their reliance on extensive datasets, possibly including
sensitive information. Regulatory measures like GDPR and CCPA call for using
robust auditing tools to address potential privacy issues, with Membership
Inference Attacks (MIA) being the primary method for assessing LLMs' privacy
risks. Differently from traditional MIA approaches, often requiring
computationally intensive training of additional models, this paper introduces
an efficient methodology that generates \textit{noisy neighbors} for a target
sample by adding stochastic noise in the embedding space, requiring operating
the target model in inference mode only. Our findings demonstrate that this
approach closely matches the effectiveness of employing shadow models, showing
its usability in practical privacy auditing scenarios.
