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20242026
most citedPosition: Adversarial ML for LLMs Is Not Making Any Progress

1 citations · 1 across the 3 of their papers we have counts for

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cs.CL2024

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…

cs.CL2024

Competition Report: Finding Universal Jailbreak Backdoors in Aligned LLMs

Javier Rando, Francesco Croce, Kryštof Mitka +4

Large language models are aligned to be safe, preventing users from generating harmful content like misinformation or instructions for illegal activities. However, previous work ha…