1 citations · 1 across the 4 of their papers we have counts for
4 papers
Tab-MIA: A Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs
Eyal German, Sagiv Antebi, Daniel Samira +2
Large language models (LLMs) are increasingly trained on tabular data, which, unlike unstructured text, often contains personally identifiable information (PII) in a highly structu…
LexiMark: Robust Watermarking via Lexical Substitutions to Enhance Membership Verification of an LLM's Textual Training Data
Eyal German, Sagiv Antebi, Edan Habler +2
Large language models (LLMs) can be trained or fine-tuned on data obtained without the owner's consent. Verifying whether a specific LLM was trained on particular data instances or…
Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack
Sagiv Antebi, Edan Habler, Asaf Shabtai +1
Large language models (LLMs) have become essential tools for digital task assistance. Their training relies heavily on the collection of vast amounts of data, which may include cop…
GPT in Sheep's Clothing: The Risk of Customized GPTs
Sagiv Antebi, Noam Azulay, Edan Habler +3
In November 2023, OpenAI introduced a new service allowing users to create custom versions of ChatGPT (GPTs) by using specific instructions and knowledge to guide the model's behav…