16 citations · 32 across the 8 of their papers we have counts for
8 papers · 1 filter
Chasing Shadows: Pitfalls in LLM Security Research
Jonathan Evertz, Niklas Risse, Nicolai Neuer +12
Large language models (LLMs) are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of re…
Prompt Obfuscation for Large Language Models
David Pape, Sina Mavali, Thorsten Eisenhofer +1
System prompts that include detailed instructions to describe the task performed by the underlying LLM can easily transform foundation models into tools and services with minimal o…
Whispers in the Machine: Confidentiality in Agentic Systems
Jonathan Evertz, Merlin Chlosta, Lea Schönherr +1
Large language model (LLM)-based agents combine LLMs with external tools to automate tasks such as scheduling meetings, managing documents, or booking travel. While these integrati…
A Representative Study on Human Detection of Artificially Generated Media Across Countries
Joel Frank, Franziska Herbert, Jonas Ricker +5
AI-generated media has become a threat to our digital society as we know it. These forgeries can be created automatically and on a large scale based on publicly available technolog…
No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning
Thorsten Eisenhofer, Erwin Quiring, Jonas Möller +3
The number of papers submitted to academic conferences is steadily rising in many scientific disciplines. To handle this growth, systems for automatic paper-reviewer assignments ar…
Dompteur: Taming Audio Adversarial Examples
Thorsten Eisenhofer, Lea Schönherr, Joel Frank +3
Adversarial examples seem to be inevitable. These specifically crafted inputs allow attackers to arbitrarily manipulate machine learning systems. Even worse, they often seem harmle…