733 citations · 899 across the 14 of their papers we have counts for
6 papers · 1 filter
Measuring Non-Adversarial Reproduction of Training Data in Large Language Models
Michael Aerni, Javier Rando, Edoardo Debenedetti +3
Large language models memorize parts of their training data. Memorizing short snippets and facts is required to answer questions about the world and to be fluent in any language. B…
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense
Jie Zhang, Christian Schlarmann, Kristina Nikolić +4
Ensemble everything everywhere is a defense to adversarial examples that was recently proposed to make image classifiers robust. This defense works by ensembling a model's intermed…
Extracting Training Data from Document-Based VQA Models
Francesco Pinto, Nathalie Rauschmayr, Florian Tramèr +2
Vision-Language Models (VLMs) have made remarkable progress in document-based Visual Question Answering (i.e., responding to queries about the contents of an input document provide…
Adversarial Search Engine Optimization for Large Language Models
Fredrik Nestaas, Edoardo Debenedetti, Florian Tramèr
Large Language Models (LLMs) are increasingly used in applications where the model selects from competing third-party content, such as in LLM-powered search engines or chatbot plug…
Dataset and Lessons Learned from the 2024 SaTML LLM Capture-the-Flag Competition
Edoardo Debenedetti, Javier Rando, Daniel Paleka +18
Large language model systems face important security risks from maliciously crafted messages that aim to overwrite the system's original instructions or leak private data. To study…
Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
Shanglun Feng, Florian Tramèr
Practitioners commonly download pretrained machine learning models from open repositories and finetune them to fit specific applications. We show that this practice introduces a ne…