6 papers
Memetic Trojans: Social Contagions as Carriers of Adversarial Payloads in Agent Networks
Birk Torpmann-Hagen, Finn Schwall, Leon Moonen
Autonomous large language model (LLM) agents increasingly interact in network environments where adversarial content can propagate between agents. Known attacks include agent worms…
When No Benchmark Exists: Validating Comparative LLM Safety Scoring Without Ground-Truth Labels
Sushant Gautam, Finn Schwall, Annika Willoch Olstad +6
Many deployments must compare candidate language models for safety before a labeled benchmark exists for the relevant language, sector, or regulatory regime. We formalize this sett…
Steering grids for sparse-autoencoder features: when a top-context label names an activation regime rather than a causal axis
Michael A. Riegler, Birk Sebastian Frostelid Torpmann-Hagen
The standard protocol for interpreting sparse-autoencoder (SAE) features labels each feature from its top-activating contexts and validates the label by steering that single featur…
Defending against Stegomalware in Deep Neural Networks with Permutation Symmetry
Birk Torpmann-Hagen, Michael A. Riegler, Pål Halvorsen +1
Deep neural networks are being utilized in a growing number of applications, both in production systems and for personal use. Network checkpoints are as a consequence often shared…
Probabilistic Runtime Verification, Evaluation and Risk Assessment of Visual Deep Learning Systems
Birk Torpmann-Hagen, Pål Halvorsen, Michael A. Riegler +1
Despite achieving excellent performance on benchmarks, deep neural networks often underperform in real-world deployment due to sensitivity to minor, often imperceptible shifts in i…
Segmentation Consistency Training: Out-of-Distribution Generalization for Medical Image Segmentation
Birk Torpmann-Hagen, Vajira Thambawita, Kyrre Glette +2
Generalizability is seen as one of the major challenges in deep learning, in particular in the domain of medical imaging, where a change of hospital or in imaging routines can lead…