3 papers
cs.CR2026
Security-by-Design for LLM-Based Code Generation: Leveraging Internal Representations for Concept-Driven Steering Mechanisms
Maximilian Wendlinger, Daniel Kowatsch, Konstantin Böttinger +1
Large Language Models (LLMs) show remarkable capabilities in understanding natural language and generating complex code. However, as practitioners adopt CodeLLMs for increasingly c…
cs.LG2025
Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning
Maximilian Wendlinger, Kilian Tscharke, Pascal Debus
One of the key obstacles in traditional deep learning is the reduction in model transparency caused by increasingly intricate model functions, which can lead to problems such as ov…
quant-ph2025
Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security
Pascal Debus, Maximilian Wendlinger, Kilian Tscharke +7
Quantum Machine Learning (QML) systems inherit vulnerabilities from classical machine learning while introducing new attack surfaces rooted in the physical and algorithmic layers o…