Nasr, Milad

Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

Nasr, M., Shokri, R., & Houmansadr, A. (2019) · 2019 IEEE Symposium on Security and Privacy (SP)

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 1,760

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Machine Learning with Membership Privacy using Adversarial Regularization

Nasr, M., Shokri, R., & Houmansadr, A. (2018) · Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 458

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Universal and Transferable Adversarial Attacks on Aligned Language Models

Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J. Z., & Fredrikson, M. (2023) · arXiv

Adversarial Robustness in Machine LearningSpeech Recognition and Synthesis

Cited by 197

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

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

Preventing generation of verbatim memorization in language models gives a false sense of privacy

Ippolito, D., Tramer, F., Nasr, M., Zhang, C., Jagielski, M., Lee, K., Choquette Choo, C., & Carlini, N. (2023) · Proceedings of the 16th International Natural Language Generation Conference

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 28

Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards

Huang, Y., Nasr, M., Angelopoulos, A., Carlini, N., Chiang, W.-L., Choquette-Choo, C. A., Ippolito, D., Jagielski, M., Lee, K., Liu, K. Z., et al. (2025) · arXiv

Adversarial Robustness in Machine LearningSpam and Phishing Detection

Cited by 17

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

ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?

Wang, Z., Schiller, N., Li, H., Narayana, S. S., Nasr, M., Carlini, N., Qi, X., Wallace, E., Bursztein, E., Invernizzi, L., et al. (2026) · arXiv

Security and Verification in ComputingAdversarial Robustness in Machine Learning

Cited by 3