--- title: "Who cites Membership Inference Attacks From First Principles" description: "82 papers of the Elamur Research library cite «Membership Inference Attacks From First Principles», found in their bibliographies; the most…" canonical: "https://research.elamur.ai/free/en/papers/carlini-2021-membership-inference-attacks-first-principles/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/carlini-2021-membership-inference-attacks-first-principles/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Membership Inference Attacks From First Principles](https://md.research.elamur.ai/free/en/papers/carlini-2021-membership-inference-attacks-first-principles/) / cited by # Who cites «Membership Inference Attacks From First Principles» 82 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · [← back to «Membership Inference Attacks From First Principles»](https://md.research.elamur.ai/free/en/papers/carlini-2021-membership-inference-attacks-first-principles/) 1. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/) 2. [**Scalable Extraction of Training Data from (Production) Language Models arXiv:2311.17035**Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/nasr-2023-scalable-extraction-training-data-production/) 3. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models arXiv:2202.06539**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/) 4. **[Membership Inference Attacks against Language Models via Neighbourhood Comparison](https://md.research.elamur.ai/free/en/papers/mattern-2023-membership-inference-attacks-against-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, et al.*2023 · 165 citations* 5. **[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/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.*2022 · 146 citations* 6. [**Do Membership Inference Attacks Work on Large Language Models? arXiv:2402.07841**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/) 7. **[Machine Unlearning of Pre-trained Large Language Models](https://md.research.elamur.ai/free/en/papers/yao-2024-machine-unlearning-pre-trained-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jin Yao, Eli Chien, Minxin Du, et al.*2024 · 86 citations* 8. **[Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/hu-2025-context-reasoner-incentivizing-reasoning-capability/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenbin Hu, Haoran Li, Huihao Jing, et al.*2025 · 7 citations* 9. **[Detecting Training Data of Large Language Models via Expectation Maximization](https://md.research.elamur.ai/free/en/papers/kim-2026-detecting-training-data-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, et al.*2026 · 5 citations* 10. **[Neural Breadcrumbs: Membership Inference Attacks on LLMs Through Hidden State and Attention Pattern Analysis](https://md.research.elamur.ai/free/en/papers/makhija-2026-neural-breadcrumbs-membership-inference-attacks/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Disha Makhija, Manoj Ghuhan Arivazhagan, Vinayshekhar Bannihatti Kumar, et al.*2026 · 2 citations* 11. **[Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization Framework](https://md.research.elamur.ai/free/en/papers/luo-2026-do-llms-really-memorize-personally/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyu Luo, Yiyi Chen, Qiongxiu Li, et al.*2026 · 1 citation* 12. **[Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-image-corruption-inspired-membership-inference/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zongyu Wu, Minhua Lin, Zhiwei Zhang, et al.*2026 · 1 citation* 13. **[The Model's Language Matters: A Comparative Privacy Analysis of LLMs](https://md.research.elamur.ai/free/en/papers/mishra-2026-model-s-language-matters-comparative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Abhishek Kumar Mishra, Antoine Boutet, Lucas Magnana*2026 · 1 citation* 14. **[Black-Box Membership Inference Attacks for Video Training Data in Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-black-box-membership-inference-attacks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinrui Wang, Zhenfeng Gao, Wendan Wang, et al.*2026* 15. **[CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-checkmiabench-firm-foundations-membership-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeffrey George Wang, Jason Wang, Marvin Li, et al.*2026* 16. **[Fast-MIA: Efficient and Scalable Membership Inference for LLMs](https://md.research.elamur.ai/free/en/papers/takahashi-2026-fast-mia-efficient-scalable-membership/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hiromu Takahashi, Shotaro Ishihara*2026* 17. **[LLMSurgeon: Diagnosing Data Mixture of Large Language Models](https://md.research.elamur.ai/free/en/papers/luo-2026-llmsurgeon-diagnosing-data-mixture-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, et al.*2026* 18. **[Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks](https://md.research.elamur.ai/free/en/papers/sarwar-2026-multimodal-unlearning-across-vision-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu, et al.*2026* 19. **[PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection](https://md.research.elamur.ai/free/en/papers/liu-2026-pdr-plug-play-positional-decay/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinhan Liu, Yibo Yang, Ruiying Lu, et al.*2026* 20. **[PII Jailbreaking in LLMs via Activation Steering Reveals Personal Information Leakage](https://md.research.elamur.ai/free/en/papers/nakka-2026-pii-jailbreaking-llms-activation-steering/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Krishna Kanth Nakka, Xue Jiang, Dmitrii Usynin, et al.*2026* 21. **[Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation](https://md.research.elamur.ai/free/en/papers/ma-2026-private-seeds-public-llms-realistic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qian Ma, Sarah Rajtmajer*2026* 22. **[SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density](https://md.research.elamur.ai/free/en/papers/yi-2026-sead-surrogate-free-label-only/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Biao Yi, Jiahao Li, Yiming Li, et al.*2026* 23. **[SLIM: Stealthy Low-Coverage Black-Box Watermarking via Latent-Space Confusion Zones](https://md.research.elamur.ai/free/en/papers/wu-2026-slim-stealthy-low-coverage-black/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyu WU, Yang Cao*2026* 59 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/carlini-2021-membership-inference-attacks-first-principles/) — Membership Inference Attacks From First Principles