| Fine-tuning aligned language models compromises safety, even when users do not intend to! X Qi, Y Zeng, T Xie, PY Chen, R Jia, P Mittal, P Henderson International Conference on Learning Representations (ICLR), 2024 (Oral), 2023 | 189 | 2023 |
| Visual Adversarial Examples Jailbreak Aligned Large Language Models X Qi, K Huang, A Panda, P Henderson, M Wang, P Mittal AAAI Conference on Artificial Intelligence, 2024 (Oral), 2023 | 106* | 2023 |
| Revisiting the assumption of latent separability for backdoor defenses X Qi, T Xie, Y Li, S Mahloujifar, P Mittal International Conference on Learning Representations (ICLR), 2023, 2023 | 81* | 2023 |
| Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks X Qi, T Xie, R Pan, J Zhu, Y Yang, K Bu Conference on Computer Vision and Pattern Recognition (CVPR), 2022 (Oral), 2021 | 54 | 2021 |
| Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attacks NM Gürel, X Qi, L Rimanic, C Zhang, B Li International Conference on Machine Learning (ICML), 2021, 2021 | 35 | 2021 |
| Towards A Proactive {ML} Approach for Detecting Backdoor Poison Samples X Qi, T Xie, JT Wang, T Wu, S Mahloujifar, P Mittal 32nd USENIX Security Symposium (USENIX Security 23), 1685-1702, 2023 | 29* | 2023 |
| Assessing the brittleness of safety alignment via pruning and low-rank modifications B Wei, K Huang, Y Huang, T Xie, X Qi, M Xia, P Mittal, M Wang, ... International Conference on Machine Learning (ICML), 2024, 2024 | 27 | 2024 |
| Subnet Replacement: Deployment-stage backdoor attack against deep neural networks in gray-box setting X Qi, J Zhu, C Xie, Y Yang ICLR Workshop, 2021 | 25 | 2021 |
| Mitigating fine-tuning jailbreak attack with backdoor enhanced alignment J Wang, J Li, Y Li, X Qi, M Chen, J Hu, Y Li, B Li, C Xiao arXiv preprint arXiv:2402.14968, 2024 | 12 | 2024 |
| BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection T Xie, X Qi, P He, Y Li, JT Wang, P Mittal International Conference on Learning Representations (ICLR), 2024, 2023 | 5 | 2023 |
| Uncovering Adversarial Risks of Test-Time Adaptation T Wu, F Jia, X Qi, JT Wang, V Sehwag, S Mahloujifar, P Mittal International Conference on Machine Learning (ICML), 2023, 2023 | 5 | 2023 |
| Safety Alignment Should Be Made More Than Just a Few Tokens Deep X Qi, A Panda, K Lyu, X Ma, S Roy, A Beirami, P Mittal, P Henderson arXiv preprint arXiv:2406.05946, 2024 | 2 | 2024 |
| Defensive prompt patch: A robust and interpretable defense of llms against jailbreak attacks C Xiong, X Qi, PY Chen, TY Ho arXiv preprint arXiv:2405.20099, 2024 | 2 | 2024 |
| Lottery Ticket Adaptation: Mitigating Destructive Interference in LLMs A Panda, B Isik, X Qi, S Koyejo, T Weissman, P Mittal arXiv preprint arXiv:2406.16797, 2024 | | 2024 |
| SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors T Xie, X Qi, Y Zeng, Y Huang, UM Sehwag, K Huang, L He, B Wei, D Li, ... arXiv preprint arXiv:2406.14598, 2024 | | 2024 |
| AI Risk Management Should Incorporate Both Safety and Security X Qi, Y Huang, Y Zeng, E Debenedetti, J Geiping, L He, K Huang, ... arXiv preprint arXiv:2405.19524, 2024 | | 2024 |
Prateek MittalProfessor, Princeton UniversityVerified email at princeton.edu
