7 papers
Adversarial Robustness of AI-Generated Image Detectors in the Real World
Sina Mavali, Jonas Ricker, David Pape +2
The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse. In particular the generation of credible misinfo…
The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility
David Pape, Jonathan Evertz, Lea Schönherr
Progress in LLMs is increasingly measured through standardized benchmarks, where state-of-the-art improvements are often separated by fractions of a percentage point. At the same t…
No More, No Less: Task Alignment in Terminal Agents
Sina Mavali, David Pape, Jonathan Evertz +5
Terminal agents are increasingly capable of executing complex, long-horizon tasks autonomously from a single user prompt. To do so, they must interpret instructions encountered in…
Unknown Unknowns: Why Hidden Intentions in LLMs Evade Detection
Devansh Srivastav, David Pape, Lea Schönherr
LLMs are increasingly embedded in everyday decision-making, yet their outputs can encode subtle, unintended behaviours that shape user beliefs and actions. We refer to these covert…
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…