Sampling-based Pseudo-Likelihood for Membership Inference Attacks (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are trained on large-scale web data, which makes it difficult to grasp the contribution of each text. |
| Approach: | They propose a membership-inference attack method that uses only the input text to detect leaks. |
| Outcome: | The proposed method performs on par with existing likelihood-based methods even without likelihoods. |
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Yuanhong Huang, Huili Wang, Xueying Bai, Jinrui Wang, Jiajun Liu, Ziqin Wang, Wanchun Ni, Shangguang Wang, Tao Qi
| Challenge: | Membership inference attacks are a promising tool for auditing training data of LLMs . existing methods rely on the assumption that LLM's assign higher confidence scores to training samples than to non-training ones. |
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Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models (2025.findings-naacl)
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| Challenge: | Membership inference attacks (MIAs) attempt to verify the membership of a data sample in the training set for a model. |
| Approach: | They propose to use membership inference attacks to verify the membership of a given data sample in a model training set. |
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Context-Aware Membership Inference Attacks against Pre-trained Large Language Models (2025.emnlp-main)
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| Challenge: | Prior Membership Inference Attacks on pre-trained Large Language Models fail at LLMs due to ignoring the generative nature of LLM data. |
| Approach: | They propose a method that adapts MIA statistical tests to the perplexity dynamics of subsequences within a data point. |
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CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models (2026.acl-short)
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| Challenge: | Membership inference attacks are a canonical way to assess a machine learning model’s privacy properties. |
| Approach: | They propose a framework for principled evaluation of membership inference attacks against large language models by leveraging the insight that training data before and after a fixed point during training are drawn from the same distribution. |
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A Statistical and Multi-Perspective Revisiting of the Membership Inference Attack in Large Language Models (2025.acl-long)
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| Challenge: | Membership Inference Attack (MIA) is a method that differentiates trained (member) and untrained (non-member) data. |
| Approach: | They used thousands of experiments to examine membership inference attacks from different settings and then revisited them with thousands of different methods. |
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Order of Magnitude Speedups for LLM Membership Inference (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are complex and require fine-tuning on proprietary datasets to improve performance and relevance. |
| Approach: | They propose a low-cost membership inference attack that leverages an ensemble of small quantile regression models to determine if a document belongs to the model’s training set. |
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ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods (2024.emnlp-main)
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| Challenge: | ReCaLL (Relative Conditional Log-Likelihood) is a membership inference attack that can detect LLMs’ pretraining data by leveraging their conditional language modeling capabilities. |
| Approach: | They propose a membership inference attack to detect LLMs’ pretraining data by leveraging their conditional language modeling capabilities. |
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Membership Inference Attacks against Language Models via Neighbourhood Comparison (2023.findings-acl)
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Justus Mattern, Fatemehsadat Mireshghallah, Zhijing Jin, Bernhard Schoelkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick
| Challenge: | Existing membership inference attacks aim to predict whether a data sample was present in training data of a machine learning model. |
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Detecting Training Data of Large Language Models via Expectation Maximization (2026.eacl-long)
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| Challenge: | Membership inference attacks aim to determine whether a specific example was used to train a given language model. |
| Approach: | They propose a membership inference approach that iteratively refines prefix effectiveness and membership scores using an expectation-maximization strategy without requiring labeled non-member examples. |
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Tag&Tab: Pretraining Data Detection in Large Language Models Using Keyword-Based Membership Inference Attack (2025.findings-emnlp)
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| Challenge: | Recent studies on detecting pretraining data in large language models have focused on sentence-level membership inference attacks (MIAs) but these methods often exhibit poor accuracy, failing to account for the semantic importance of textual content and word significance. |
| Approach: | They propose a method that leverages established natural language processing techniques to tag keywords in input text and then uses them to obtain probabilities and calculate their average log-likelihood to determine input text membership. |
| Outcome: | The proposed method exploits established natural language processing techniques to tag keywords in input text and calculate their average log-likelihood to determine input text membership. |