27 citations · 110 across the 41 of their papers we have counts for
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cs.AI2025
AdversariaLLM: A Unified and Modular Toolbox for LLM Robustness Research
Tim Beyer, Jonas Dornbusch, Jakob Steimle +3
The rapid expansion of research on Large Language Model (LLM) safety and robustness has produced a fragmented and oftentimes buggy ecosystem of implementations, datasets, and evalu…
cs.AI2023★ 12 cited
Adversarial Attacks and Defenses in Large Language Models: Old and New Threats
Leo Schwinn, David Dobre, Stephan Günnemann +1
Over the past decade, there has been extensive research aimed at enhancing the robustness of neural networks, yet this problem remains vastly unsolved. Here, one major impediment h…