Mental Models of Adversarial Machine Learning. - Lukas Bieringer; Kathrin Grosse; Michael Backes 001; Katharina Krombholz (CoRR, 2021)

Industrial practitioners' mental models of adversarial machine learning. - Lukas Bieringer; Kathrin Grosse; Michael Backes 001; Battista Biggio; Katharina Krombholz (SOUPS @ USENIX Security Symposium, 2022)

"Why do so?" - A Practical Perspective on Machine Learning Security. - Kathrin Grosse; Lukas Bieringer; Tarek Richard Besold; Battista Biggio; Katharina Krombholz (CoRR, 2022)

Machine Learning Security in Industry: A Quantitative Survey. - Kathrin Grosse; Lukas Bieringer; Tarek R. Besold; Battista Biggio; Katharina Krombholz (IEEE Trans. Inf. Forensics Secur., 2023)

When Your AI Becomes a Target: AI Security Incidents and Best Practices. - Kathrin Grosse; Lukas Bieringer; Tarek R. Besold; Battista Biggio; Alexandre Alahi (AAAI, 2024)

Towards More Practical Threat Models in Artificial Intelligence Security. - Kathrin Grosse; Lukas Bieringer; Tarek R. Besold; Alexandre Alahi (USENIX Security Symposium, 2024)

Position: A taxonomy for reporting and describing AI security incidents. - Lukas Bieringer; Kevin Paeth; Andreas Wespi; Kathrin Grosse (CoRR, 2024)

On challenges and approaches to test AI systems. - Antoine Gautier 005; Lukas Bieringer; Stefan Klößner (Datenschutz und Datensicherheit (dud), 2025)