| 2026 | ACL | MASEval: Extending Multi-Agent Evaluation from Models to Systems. | Cornelius Emde, Alexander Rubinstein, Anmol Goel, Ahmed Heakl, Sangdoo Yun, Seong Joon Oh, Martin Gubri |
| 2026 | ACL | Privacy Collapse: Benign Fine-Tuning Can Break Contextual Privacy in Language Models. | Anmol Goel, Cornelius Emde, Seong Joon Oh, Sangdoo Yun, Martin Gubri |
| 2025 | EMNLP | Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers. | Tommaso Green, Martin Gubri, Haritz Puerto, Sangdoo Yun, Seong Joon Oh |
| 2025 | NAACL | Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models. | Haritz Puerto, Martin Gubri, Sangdoo Yun, Seong Joon Oh |
| 2024 | ACL | TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification. | Martin Gubri, Dennis Ulmer, Hwaran Lee, Sangdoo Yun, Seong Joon Oh |
| 2024 | ACL | Calibrating Large Language Models Using Their Generations Only. | Dennis Ulmer, Martin Gubri, Hwaran Lee, Sangdoo Yun, Seong Joon Oh |
| 2022 | CAIN | Influence-driven data poisoning in graph-based semi-supervised classifiers. | Adriano Franci, Maxime Cordy, Martin Gubri, Mike Papadakis, Yves Le Traon |
| 2022 | ECCV | LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity. | Martin Gubri, Maxime Cordy, Mike Papadakis, Yves Le Traon, Koushik Sen |
| 2022 | UAI | Efficient and transferable adversarial examples from bayesian neural networks. | Martin Gubri, Maxime Cordy, Mike Papadakis, Yves Le Traon, Koushik Sen |