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Matthieu Meeus

3 accepted papers

2025

Exploring the limits of strong membership inference attacks on large language models

NeurIPS 2025poster

State-of-the-art membership inference attacks (MIAs) typically require training many reference models, making it difficult to scale these attacks to large pre-trained language models (LLMs). As a result, prior research has either relied on weaker attacks that avoid training references (e.g., fine-tu…

Cited by 0SourceScholar
2025

The Canary’s Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text

ICML 2025poster

How much information about training samples can be leaked through synthetic data generated by Large Language Models (LLMs)? Overlooking the subtleties of information flow in synthetic data generation pipelines can lead to a false sense of privacy. In this paper, we assume an adversary has access to…

Cited by 0SourcePDFScholar
2024

Copyright Traps for Large Language Models

ICML 2024poster

Questions of fair use of copyright-protected content to train Large Language Models (LLMs) are being actively debated. Document-level inference has been proposed as a new task: inferring from black-box access to the trained model whether a piece of content has been seen during training. SOTA methods…