7 papers · 1 filter
FloatDoor: Platform-Triggered Backdoors in LLMs
Nils Loose, Jonas Sander, Felix Mächtle +1
Large language models (LLMs) are increasingly deployed in sensitive settings such as software engineering, where their outputs directly shape downstream artifacts. Recent work has…
Steganography Without Modification: Hidden Communication via LLM Seeds
Felix Mächtle, Jonas Sander, Sebastian Berndt +3
We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model weights, sampling code, or ou…
TrEEStealer: Stealing Decision Trees via Enclave Side Channels
Jonas Sander, Anja Rabich, Nick Mahling +5
Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine current business models where a…
Prompt Pirates Need a Map: Stealing Seeds helps Stealing Prompts
Felix Mächtle, Ashwath Shetty, Jonas Sander +3
Diffusion models have significantly advanced text-to-image generation, enabling the creation of highly realistic images conditioned on textual prompts and seeds. Given the consider…
BarkBeetle: Stealing Decision Tree Models with Fault Injection
Qifan Wang, Jonas Sander, Minmin Jiang +2
Machine learning models, particularly decision trees (DTs), are widely adopted across various domains due to their interpretability and efficiency. However, as ML models become inc…
ReDASH: Fast and efficient Scaling in Arithmetic Garbled Circuits for Secure Outsourced Inference
Felix Maurer, Jonas Sander, Thomas Eisenbarth
ReDash extends Dash's arithmetic garbled circuits to provide a more flexible and efficient framework for secure outsourced inference. By introducing a novel garbled scaling gadget…