Tramèr, Florian

Advances and Open Problems in Federated Learning

Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2019) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 7,857

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

Ensemble Adversarial Training: Attacks and Defenses

Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I., Boneh, D., & McDaniel, P. (2017) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 2,176

Stealing Machine Learning Models via Prediction APIs

Tramèr, F., Zhang, F., Juels, A., Reiter, M. K., & Ristenpart, T. (2016) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 796

FairTest: Discovering Unwarranted Associations in Data-Driven Applications

Tramèr, F., Atlidakis, V., Geambasu, R., Hsu, D., Hubaux, J.-P., Humbert, M., Juels, A., & Lin, H. (2015) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 171

What Does it Mean for a Language Model to Preserve Privacy?

Brown, H., Lee, K., Mireshghallah, F., Shokri, R., & Tramèr, F. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Privacy-Preserving Technologies in DataEthics and Social Impacts of AI

Cited by 165

Adversarial training and robustness for multiple perturbations

Tramèr, F., & Boneh, D. (2019) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 145

Poisoning Web-Scale Training Datasets is Practical

Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., & Tramèr, F. (2024) · 2024 IEEE Symposium on Security and Privacy (SP)

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 138

Poisoning Web-Scale Training Datasets is Practical

Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., & Tramèr, F. (2023) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 138

JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

Chao, P., Debenedetti, E., Robey, A., Andriushchenko, M., Croce, F., Sehwag, V., Dobriban, E., Flammarion, N., Pappas, G., Tramèr, F., et al. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 116

Scalable Extraction of Training Data from (Production) Language Models

Nasr, M., Carlini, N., Hayase, J., Jagielski, M., Cooper, A. F., Ippolito, D., Choquette-Choo, C. A., Wallace, E., Tramèr, F., & Lee, K. (2023) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 96

Counterfactual Memorization in Neural Language Models

Zhang, C., Ippolito, D., Lee, K., Jagielski, M., Tramèr, F., & Carlini, N. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 54

International AI Safety Report

Bengio, Y., Mindermann, S., Privitera, D., Besiroglu, T., Bommasani, R., Casper, S., Choi, Y., Fox, P., Garfinkel, B., Goldfarb, D., et al. (2025) · arXiv

Ethics and Social Impacts of AISafety Systems Engineering in Autonomy

Cited by 16

Defeating Prompt Injections by Design

Debenedetti, E., Shumailov, I., Fan, T., Hayes, J., Carlini, N., Fabian, D., Kern, C., Shi, C., Terzis, A., & Tramèr, F. (2026) · 2026 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 15

Universal Jailbreak Backdoors from Poisoned Human Feedback

Rando, J., & Tramèr, F. (2023) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 10

Privacy Side Channels in Machine Learning Systems

Debenedetti, E., Severi, G., Carlini, N., Choquette-Choo, C. A., Jagielski, M., Nasr, M., Wallace, E., & Tramèr, F. (2023) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 6

Design Patterns for Securing LLM Agents against Prompt Injections

Beurer-Kellner, L., Buesser, B., Creţu, A.-M., Debenedetti, E., Dobos, D., Fabian, D., Fischer, M., Froelicher, D., Grosse, K., Naeff, D., et al. (2025) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 2

Adversarial Search Engine Optimization for Large Language Models

Nestaas, F., Debenedetti, E., & Tramèr, F. (2024) · arXiv

Adversarial Robustness in Machine LearningSpam and Phishing Detection

Cited by 1

CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents

Foerster, H., Blanchard, T., Nikolić, K., Shumailov, I., Zhang, C., Mullins, R., Papernot, N., Tramèr, F., & Zhao, Y. (2026) · arXiv

Security and Verification in ComputingAdversarial Robustness in Machine Learning

Cited by 0

Trojan Hippo Bench: A Dynamic Benchmark for Persistent Memory Attacks and Defenses in LLM Agents

Das, D., Piet, J., Kaviani, D., Beurer-Kellner, L., Tramèr, F., & Wagner, D. (2026) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 0