5 papers
Towards Reliable Local Security Agents: Verifiable Post-Training for Linux Privilege Escalation
Philipp Normann, Andreas Happe, Jürgen Cito +1
LLM agents are becoming increasingly important in the security domain, but leading systems are often closed-source, cloud-based, hard to reproduce or use with sensitive code. This…
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…
Identifying Offline Metrics that Predict Online Impact: A Pragmatic Strategy for Real-World Recommender Systems
Timo Wilm, Philipp Normann
A critical challenge in recommender systems is to establish reliable relationships between offline and online metrics that predict real-world performance. Motivated by recent advan…
Pareto Front Approximation for Multi-Objective Session-Based Recommender Systems
Timo Wilm, Philipp Normann, Felix Stepprath
This work introduces MultiTRON, an approach that adapts Pareto front approximation techniques to multi-objective session-based recommender systems using a transformer neural networ…
Scaling Session-Based Transformer Recommendations using Optimized Negative Sampling and Loss Functions
Timo Wilm, Philipp Normann, Sophie Baumeister +1
This work introduces TRON, a scalable session-based Transformer Recommender using Optimized Negative-sampling. Motivated by the scalability and performance limitations of prevailin…