140 citations · 244 across the 21 of their papers we have counts for
3 papers · 1 filter
Exposing the Systematic Vulnerability of Open-Weight Models to Prefill Attacks
Lukas Struppek, Adam Gleave, Kellin Pelrine
As the capabilities of large language models continue to advance, so does their potential for misuse. While closed-source models typically rely on external defenses, open-weight mo…
Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data
Lukas Struppek, Martin B. Hentschel, Clifton Poth +2
Backdoor attacks pose a serious security threat for training neural networks as they surreptitiously introduce hidden functionalities into a model. Such backdoors remain silent dur…
Combining AI and AM - Improving Approximate Matching through Transformer Networks
Frieder Uhlig, Lukas Struppek, Dominik Hintersdorf +3
Approximate matching (AM) is a concept in digital forensics to determine the similarity between digital artifacts. An important use case of AM is the reliable and efficient detecti…