4 citations · 7 across the 5 of their papers we have counts for
5 papers
LLM-Based Agents for Identifying Bug-Introducing Commits
Niklas Risse, Marcel Böhme
Śliwerski, Zimmermann, and Zeller (SZZ) just won the 2026 ACM SIGSOFT Impact Award for asking: When do changes induce fixes? Their paper from 2005 served as the foundation for a wi…
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…
Top Score on the Wrong Exam: On Benchmarking in Machine Learning for Vulnerability Detection
Niklas Risse, Jing Liu, Marcel Böhme
According to our survey of machine learning for vulnerability detection (ML4VD), 9 in every 10 papers published in the past five years define ML4VD as a function-level binary class…
Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection
Niklas Risse, Marcel Böhme
Recent results of machine learning for automatic vulnerability detection (ML4VD) have been very promising. Given only the source code of a function , ML4VD techniques can decide…
How to compare adversarial robustness of classifiers from a global perspective
Niklas Risse, Christina Göpfert, Jan Philip Göpfert
Adversarial robustness of machine learning models has attracted considerable attention over recent years. Adversarial attacks undermine the reliability of and trust in machine lear…