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Exploring the limits of strong membership inference attacks on large language models
Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo +13
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 mode…
Checkpoint-GCG: Auditing and Attacking Fine-Tuning-Based Prompt Injection Defenses
Xiaoxue Yang, Bozhidar Stevanoski, Matthieu Meeus +1
Large language models (LLMs) are increasingly deployed in real-world applications ranging from chatbots to agentic systems, where they are expected to process untrusted data and fo…
Synthetic is all you need: removing the auxiliary data assumption for membership inference attacks against synthetic data
Florent Guépin, Matthieu Meeus, Ana-Maria Cretu +1
Synthetic data is emerging as one of the most promising solutions to share individual-level data while safeguarding privacy. While membership inference attacks (MIAs), based on sha…