Matthew Jagielski

Most-cited papers

Extracting Training Data from Large Language Models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski +8 more
2020USENIX Security Symposium3,496 citations
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PaLM 2 Technical Report
Rohan Anil, Andrew M. Dai, O. Firat, Melvin Johnson +124 more
2023arXiv.org1,569 citations
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Extracting Training Data from Diffusion Models
Nicholas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski +5 more
2023USENIX Security Symposium1,094 citations Open access
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Quantifying Memorization Across Neural Language Models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee +2 more
2022International Conference on Learning Representations1,035 citations
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Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, B. Biggio, Chang Liu +2 more
2018IEEE Symposium on Security and Privacy903 citations Open access
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Scalable Extraction of Training Data from (Production) Language Models
Milad Nasr, Nicholas Carlini, J. Hayase, Matthew Jagielski +6 more
2023arXiv.org631 citations
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High Accuracy and High Fidelity Extraction of Neural Networks
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alexey Kurakin +1 more
2019USENIX Security Symposium513 citations
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Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Ambra Demontis, Marco Melis, Maura Pintor, Matthew Jagielski +4 more
2018USENIX Security Symposium502 citations
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Are aligned neural networks adversarially aligned?
Nicholas Carlini, Milad Nasr, Christopher A. Choquette-Choo, Matthew Jagielski +7 more
2023Neural Information Processing Systems420 citations Open access
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Poisoning Web-Scale Training Datasets is Practical
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka +5 more
2023IEEE Symposium on Security and Privacy412 citations Open access
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Auditing Differentially Private Machine Learning: How Private is Private SGD?
Matthew Jagielski, Jonathan Ullman, Alina Oprea
2020Neural Information Processing Systems359 citations
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Stealing Part of a Production Language Model
Nicholas Carlini, Daniel Paleka, K. Dvijotham, Thomas Steinke +10 more
2024International Conference on Machine Learning220 citations
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Counterfactual Memorization in Neural Language Models
Chiyuan Zhang, Daphne Ippolito, Katherine Lee, Matthew Jagielski +2 more
2021Neural Information Processing Systems219 citations Open access
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Privacy Auditing with One (1) Training Run
T. Steinke, Milad Nasr, Matthew Jagielski
2023Neural Information Processing Systems183 citations Open access
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Cryptanalytic Extraction of Neural Network Models
Nicholas Carlini, Matthew Jagielski, Ilya Mironov
2020Annual International Cryptology Conference181 citations Open access
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The Privacy Onion Effect: Memorization is Relative
Nicholas Carlini, Matthew Jagielski, Chiyuan Zhang, Nicolas Papernot +2 more
2022Neural Information Processing Systems176 citations Open access
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Differentially Private Fair Learning
Matthew Jagielski, Michael Kearns, Jieming Mao, Alina Oprea +3 more
2018International Conference on Machine Learning176 citations
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Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets
Florian Tramèr, R. Shokri, Ayrton San Joaquin, Hoang M. Le +3 more
2022Conference on Computer and Communications Security163 citations Open access
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Measuring Forgetting of Memorized Training Examples
Matthew Jagielski, Om Thakkar, Florian Tramèr, Daphne Ippolito +7 more
2022International Conference on Learning Representations160 citations Open access
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Preventing Verbatim Memorization in Language Models Gives a False Sense of Privacy
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang +4 more
2022arXiv.org149 citations Open access
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Subpopulation Data Poisoning Attacks
Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea
2020Conference on Computer and Communications Security149 citations Open access
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Scalable Extraction of Training Data from Aligned, Production Language Models
Milad Nasr, Javier Rando, Nicholas Carlini, J. Hayase +6 more
2025International Conference on Learning Representations145 citations
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Backdoor Attacks for In-Context Learning with Language Models
Nikhil Kandpal, Matthew Jagielski, Florian Tramèr, Nicholas Carlini
2023arXiv.org143 citations Open access
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Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang +4 more
2022International Conference on Natural Language Generation129 citations Open access
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This paper is included in the Proceedings of the 32nd USENIX Security Symposium.
Milad Nasr, Jamie Hayes, T. Steinke, Borja Balle +6 more
94 citations
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On Evaluating the Durability of Safeguards for Open-Weight LLMs
Xiangyu Qi, Boyi Wei, Nicholas Carlini, Yangsibo Huang +6 more
2024International Conference on Learning Representations67 citations
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Debugging Differential Privacy: A Case Study for Privacy Auditing
Florian Tramèr, A. Terzis, T. Steinke, Shuang Song +2 more
2022arXiv.org67 citations
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Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu +20 more
202366 citations
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UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
Ilia Shumailov, Jamie Hayes, Eleni Triantafillou, Guillermo Ortiz-Jiménez +5 more
2024arXiv.org65 citations
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Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation
Harsh Chaudhari, Giorgio Severi, John Abascal, Anshuman Suri +5 more
202462 citations
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Privacy Side Channels in Machine Learning Systems
Edoardo Debenedetti, Giorgio Severi, Nicholas Carlini, Christopher A. Choquette-Choo +4 more
2023USENIX Security Symposium62 citations Open access
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Students Parrot Their Teachers: Membership Inference on Model Distillation
Matthew Jagielski, Milad Nasr, Katherine Lee, Christopher A. Choquette-Choo +2 more
2023Neural Information Processing Systems55 citations Open access
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Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice
A. Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Matthew Jagielski +31 more
2024arXiv.org49 citations
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Threat Detection for Collaborative Adaptive Cruise Control in Connected Cars
Matthew Jagielski, Nicholas Jones, Chung-Wei Lin, C. Nita-Rotaru +1 more
2018Wireless Network Security45 citations
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SNAP: Efficient Extraction of Private Properties with Poisoning
Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski +2 more
2022IEEE Symposium on Security and Privacy44 citations Open access
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High-Fidelity Extraction of Neural Network Models
Matthew Jagielski, Nicholas Carlini, David Berthelot, Alexey Kurakin +1 more
2019arXiv.org37 citations
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Advancing Differential Privacy: Where We Are Now and Future Directions for Real-World Deployment
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu +20 more
2024Issue 6.1, Winter 202433 citations Open access
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Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
Ken Liu, Christopher A. Choquette-Choo, Matthew Jagielski, P. Kairouz +3 more
2025International Conference on Machine Learning32 citations
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Challenges towards the Next Frontier in Privacy
Rachel Cummings, Damien Desfontaines, David Evans, Roxana Geambasu +20 more
2023arXiv.org29 citations Open access
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Privacy-Preserving Recommender Systems with Synthetic Query Generation using Differentially Private Large Language Models
Aldo Gael Carranza, Rezsa Farahani, N. Ponomareva, Alexey Kurakin +2 more
2023arXiv.org28 citations Open access
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Exploring the limits of strong membership inference attacks on large language models
Jamie Hayes, Ilia Shumailov, Christopher A. Choquette-Choo, Matthew Jagielski +11 more
2025Neural Information Processing Systems25 citations
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Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
Yangsibo Huang, Milad Nasr, A. Angelopoulos, Nicholas Carlini +9 more
2025International Conference on Machine Learning24 citations
Semantic Scholar
Network and System Level Security in Connected Vehicle Applications
Hengyi Liang, Matthew Jagielski, Bowen Zheng, Chung-Wei Lin +4 more
20182018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)23 citations Open access
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Secure Communication Channel Establishment: TLS 1.3 (over TCP Fast Open) vs. QUIC
Shan Chen, S. Jero, Matthew Jagielski, A. Boldyreva +1 more
2019IACR Cryptology ePrint Archive21 citations
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Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models
Aldo G. Carranza, Rezsa Farahani, N. Ponomareva, Alexey Kurakin +2 more
2023North American Chapter of the Association for Computational Linguistics20 citations Open access
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Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
A. Cooper, Christopher A. Choquette-Choo, Miranda Bogen, Kevin Klyman +33 more
2024Neural Information Processing Systems19 citations Open access
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Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training
Jaydeep Borkar, Matthew Jagielski, Katherine Lee, Niloofar Mireshghallah +2 more
2025Annual Meeting of the Association for Computational Linguistics18 citations
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Network-Level Adversaries in Federated Learning
Giorgio Severi, Matthew Jagielski, Gökberk Yar, Yuxuan Wang +2 more
2022IEEE Conference on Communications and Network Security18 citations Open access
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The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
Thomas Steinke, Milad Nasr, Arun Ganesh, Borja Balle +6 more
2024International Conference on Learning Representations16 citations
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How to Combine Membership-Inference Attacks on Multiple Updated Machine Learning Models
Matthew Jagielski, Stanley Wu, Alina Oprea, Jonathan Ullman +1 more
2023Proceedings on Privacy Enhancing Technologies14 citations Open access
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