An Argument-based Approach to Mining Opinions from Twitter. - Kathrin Grosse; Carlos Iván Chesñevar; Ana Gabriela Maguitman (AT, 2012)

Empowering an E-Government Platform Through Twitter-Based Arguments. - Kathrin Grosse; Carlos Iván Chesñevar; Ana Gabriela Maguitman; Elsa Estevez (Inteligencia Artif., 2012)

A First Approach to Mining Opinions as Multisets through Argumentation. - Carlos Iván Chesñevar; María Paula González; Kathrin Grosse; Ana Gabriela Maguitman (AT, 2013)

Integrating argumentation and sentiment analysis for mining opinions from Twitter. - Kathrin Grosse; María Paula González; Carlos Iván Chesñevar; Ana Gabriela Maguitman (AI Commun., 2015)

Adversarial Perturbations Against Deep Neural Networks for Malware Classification. - Kathrin Grosse; Nicolas Papernot; Praveen Manoharan 001; Michael Backes 001; Patrick D. McDaniel (CoRR, 2016)

Adversarial Examples for Malware Detection. - Kathrin Grosse; Nicolas Papernot; Praveen Manoharan 001; Michael Backes 001; Patrick D. McDaniel (ESORICS (2), 2017)

On the (Statistical) Detection of Adversarial Examples. - Kathrin Grosse; Praveen Manoharan 001; Nicolas Papernot; Michael Backes 001; Patrick D. McDaniel (CoRR, 2017)

Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification. - Kathrin Grosse; Michael T. Smith 003; Michael Backes 001 (CoRR, 2018)

The Limitations of Model Uncertainty in Adversarial Settings. - Kathrin Grosse; David Pfaff; Michael T. Smith 003; Michael Backes 001 (CoRR, 2018)

Adversarial Initialization - when your network performs the way I want. - Kathrin Grosse; Thomas Alexander Trost; Marius Mosbach; Michael Backes 001; Dietrich Klakow (CoRR, 2019)

On the Security Relevance of Initial Weights in Deep Neural Networks. - Kathrin Grosse; Thomas Alexander Trost; Marius Mosbach; Michael Backes 001; Dietrich Klakow (ICANN (1), 2020)

Killing Four Birds with one Gaussian Process: The Relation between different Test-Time Attacks. - Kathrin Grosse; Michael T. Smith 003; Michael Backes 001 (ICPR, 2020)

A new measure for overfitting and its implications for backdooring of deep learning. - Kathrin Grosse; Taesung Lee; Youngja Park; Michael Backes 001; Ian M. Molloy (CoRR, 2020)

How many winning tickets are there in one DNN? - Kathrin Grosse; Michael Backes 001 (CoRR, 2020)

Adversarial Examples and Metrics. - Nico Döttling; Kathrin Grosse; Michael Backes 001; Ian M. Molloy (CoRR, 2020)

Why is Machine Learning Security so hard? - Kathrin Grosse (2020)

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service. - Lucjan Hanzlik; Yang Zhang 016; Kathrin Grosse; Ahmed Salem 001; Maximilian Augustin; Michael Backes 001; Mario Fritz (CVPR Workshops, 2021)

Do winning tickets exist before DNN training? - Kathrin Grosse; Michael Backes 001 (SDM, 2021)

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)

Backdoor smoothing: Demystifying backdoor attacks on deep neural networks. - Kathrin Grosse; Taesung Lee; Battista Biggio; Youngja Park; Michael Backes 001; Ian M. Molloy (Comput. Secur., 2022)

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

A Survey on Reinforcement Learning Security with Application to Autonomous Driving. - Ambra Demontis; Maura Pintor; Luca Demetrio; Kathrin Grosse; Hsiao-Ying Lin; Chengfang Fang; Battista Biggio; Fabio Roli (CoRR, 2022)

Manipulating Trajectory Prediction with Backdoors. - Kaouther Messaoud; Kathrin Grosse; Mickaël Chen; Matthieu Cord; Patrick Pérez; Alexandre Alahi (CoRR, 2023)

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning. - Antonio Emanuele Cinà; Kathrin Grosse; Ambra Demontis; Sebastiano Vascon; Werner Zellinger; Bernhard Alois Moser; Alina Oprea; Battista Biggio; Marcello Pelillo; Fabio Roli (ACM Comput. Surv., 2023)

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)

Adversarial vulnerability bounds for Gaussian process classification. - Michael Thomas Smith; Kathrin Grosse; Michael Backes 001; Mauricio A. Álvarez (Mach. Learn., 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)

Voices from the Frontline: Revealing the AI Practitioners' viewpoint on the European AI Act. - Fiona Koh; Kathrin Grosse; Giovanni Apruzzese (HICSS, 2024)

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

Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness. - David Fernández Llorca; Ronan Hamon; Henrik Junklewitz; Kathrin Grosse; Lars Kunze; Patrick Seiniger; Robert Swaim; Nick Reed; Alexandre Alahi; Emilia Gómez; Ignacio Sánchez; Ákos Kriston (CoRR, 2024)

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

Rethinking data augmentation for adversarial robustness. - Hamid Eghbal-zadeh; Werner Zellinger; Maura Pintor; Kathrin Grosse; Khaled Koutini; Bernhard Alois Moser; Battista Biggio; Gerhard Widmer (Inf. Sci., 2024)

Machine Learning Security Against Data Poisoning: Are We There Yet? - Antonio Emanuele Cinà; Kathrin Grosse; Ambra Demontis; Battista Biggio; Fabio Roli; Marcello Pelillo (Computer, 2024)

Backdoor learning curves: explaining backdoor poisoning beyond influence functions. - Antonio Emanuele Cinà; Kathrin Grosse; Sebastiano Vascon; Ambra Demontis; Battista Biggio; Fabio Roli; Marcello Pelillo (Int. J. Mach. Learn. Cybern., 2025)

Design Patterns for Securing LLM Agents against Prompt Injections. - Luca Beurer-Kellner; Beat Buesser; Ana-Maria Cretu 001; Edoardo Debenedetti; Daniel Dobos; Daniel Fabian; Marc Fischer 002; David Froelicher; Kathrin Grosse; Daniel Naeff; Ezinwanne Ozoani; Andrew Paverd; et al. (CoRR, 2025)