---
title: "Who cites Quantifying Privacy Risks of — 53 citing papers"
description: "53 papers of the Elamur Research library cite «Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks»…"
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---
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# Who cites «Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks»

53 papers of our library cite it (of the 141,840 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks»](https://md.research.elamur.ai/free/en/papers/mireshghallah-2022-quantifying-privacy-risks-masked-language/)

1. [**A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly**Yifan Yao, Jinhao Duan, Kaidi Xu, et al.*2023 · 1,182 citations*](https://md.research.elamur.ai/free/en/papers/yao-2023-survey-large-language-model-llm/)
2. [**Security and Privacy Challenges of Large Language Models: A Survey**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 422 citations*](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/)
3. [**"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models**Xinyue Shen, Zeyuan Chen, Michael Backes, et al.*2023 · 320 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-anything-now-characterizing-evaluating-wild/)
4. [**Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models**Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/sengupta-2023-jais-jais-chat-arabic-centric/)
5. [**Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 183 citations*](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/)
6. [**Membership Inference Attacks against Language Models via Neighbourhood Comparison**Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/)
7. [**Large Language Model Alignment: A Survey**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
8. [**Do Membership Inference Attacks Work on Large Language Models?**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/)

45 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/mireshghallah-2022-quantifying-privacy-risks-masked-language/) — Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks