14 papers
Claudini: Autoresearch Discovers State-of-the-Art Adversarial Attack Algorithms for LLMs
Alexander Panfilov, Peter Romov, Igor Shilov +3
We show that AI agents are capable of discovering novel algorithms for adversarial attacks against LLMs, advancing the state of the art on white-box jailbreaking and prompt injecti…
RAT-Bench: A Comprehensive Benchmark for Text Anonymization
NataÅ¡a KrÄo, Zexi Yao, Matthieu Meeus +1
Data containing personal information is increasingly used to train, fine-tune, or query Large Language Models (LLMs). Text is typically scrubbed of identifying information prior to…
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…
The Tail Tells All: Estimating Model-Level Membership Inference Vulnerability Without Reference Models
Euodia Dodd, NataÅ¡a KrÄo, Igor Shilov +1
Membership inference attacks (MIAs) have emerged as the standard tool for evaluating the privacy risks of AI models. However, state-of-the-art attacks require training numerous, of…
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…
Lost in the Averages: A New Specific Setup to Evaluate Membership Inference Attacks Against Machine Learning Models
NataÅ¡a KrÄo, Florent Guépin, Matthieu Meeus +2
Synthetic data generators and machine learning models can memorize their training data, posing privacy concerns. Membership inference attacks (MIAs) are a standard method of estima…