Automatic Calibration for Membership Inference Attack on Large Language Models

• URL: http://arxiv.org/abs/2505.03392v1
• Date: Tue, 06 May 2025 10:15:05 GMT
• Title: Automatic Calibration for Membership Inference Attack on Large Language Models
• Abstract summary: We introduce a novel framework called Automatic Membership Inference Attack (ACMIA)<n>ACMIA calibrates a tunable temperature to output probabilities effectively.<n>Our proposed attack is highly effective, robust, and generalizable.
• Score: 11.497378121062251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Membership Inference Attacks (MIAs) have recently been employed to determine whether a specific text was part of the pre-training data of Large Language Models (LLMs). However, existing methods often misinfer non-members as members, leading to a high false positive rate, or depend on additional reference models for probability calibration, which limits their practicality. To overcome these challenges, we introduce a novel framework called Automatic Calibration Membership Inference Attack (ACMIA), which utilizes a tunable temperature to calibrate output probabilities effectively. This approach is inspired by our theoretical insights into maximum likelihood estimation during the pre-training of LLMs. We introduce ACMIA in three configurations designed to accommodate different levels of model access and increase the probability gap between members and non-members, improving the reliability and robustness of membership inference. Extensive experiments on various open-source LLMs demonstrate that our proposed attack is highly effective, robust, and generalizable, surpassing state-of-the-art baselines across three widely used benchmarks. Our code is available at: \href{https://github.com/Salehzz/ACMIA}{\textcolor{blue}{Github}}.

Related papers

• On Predicting the Post-training Potential of Pre-trained LLMs [60.07459271263409]
  We introduce a new task of predicting post-training potential - forecasting a base model's performance before post-training.<n>We propose RuDE, a unified framework that bypasses the generation gap of base models by leveraging response discrimination.<n>Experiments demonstrate a correlation greater than 90% with post-training performance.
  arXiv  Detail & Related papers  (2026-05-12T11:33:49Z)
• Robust LLM Performance Certification via Constrained Maximum Likelihood Estimation [14.465019747508604]
  We propose a new, practical, and efficient approach to LLM failure rate estimation based on constrained maximum-likelihood estimation (MLE)<n>We validate our approach through a comprehensive empirical study, benchmarking it against state-of-the-art baselines like Prediction-Powered Inference (PPI)
  arXiv  Detail & Related papers  (2026-03-11T21:48:40Z)
• What Hard Tokens Reveal: Exploiting Low-confidence Tokens for Membership Inference Attacks against Large Language Models [2.621142288968429]
  Membership Inference Attacks (MIAs) attempt to determine whether a specific data sample was included in a model training/fine-tuning dataset.<n>We propose a novel membership inference approach that captures the token-level probabilities for low-confidence (hard) tokens.<n>Experiments on both domain-specific medical datasets and general-purpose benchmarks demonstrate that HT-MIA consistently outperforms seven state-of-the-art MIA baselines.
  arXiv  Detail & Related papers  (2026-01-27T22:31:10Z)
• In-Context Probing for Membership Inference in Fine-Tuned Language Models [14.590625376049955]
  Membership inference attacks (MIAs) pose a critical privacy threat to fine-tuned large language models (LLMs)<n>We propose ICP-MIA, a novel MIA framework grounded in the theory of training dynamics.<n>ICP-MIA significantly outperforms prior black-box MIAs, particularly at low false positive rates.
  arXiv  Detail & Related papers  (2025-12-18T08:26:26Z)
• Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes [54.93980123979578]
  We introduce Latent Preference Coding (LPC), a novel framework that models the implicit factors as well as their combinations behind holistic preferences.<n>LPC seamlessly integrates with various offline alignment algorithms, automatically inferring the underlying factors and their importance from data.
  arXiv  Detail & Related papers  (2025-05-08T06:59:06Z)
• EM-MIAs: Enhancing Membership Inference Attacks in Large Language Models through Ensemble Modeling [2.494935495983421]
  This paper proposes a novel ensemble attack method that integrates several existing MIAs techniques into an XGBoost-based model to enhance overall attack performance (EM-MIAs)<n> Experimental results demonstrate that the ensemble model significantly improves both AUC-ROC and accuracy compared to individual attack methods across various large language models and datasets.
  arXiv  Detail & Related papers  (2024-12-23T03:47:54Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications.<n> Ensuring their alignment with the diverse preferences of individual users has become a critical challenge.<n>We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• Accelerating Large Language Model Inference with Self-Supervised Early Exits [0.0]
  This paper presents a novel technique for accelerating inference in large, pre-trained language models (LLMs) We propose the integration of early exit ''heads'' atop existing transformer layers, which facilitate conditional terminations based on a confidence metric.
  arXiv  Detail & Related papers  (2024-07-30T07:58:28Z)
• ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods [56.073335779595475]
  We propose ReCaLL (Relative Conditional Log-Likelihood), a novel membership inference attack (MIA) ReCaLL examines the relative change in conditional log-likelihoods when prefixing target data points with non-member context. We conduct comprehensive experiments and show that ReCaLL achieves state-of-the-art performance on the WikiMIA dataset.
  arXiv  Detail & Related papers  (2024-06-23T00:23:13Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Tiny Refinements Elicit Resilience: Toward Efficient Prefix-Model Against LLM Red-Teaming [37.32997502058661]
  This paper introduces the textbfsentinel model as a plug-and-play prefix module designed to reconstruct the input prompt with just a few tokens. The sentinel model naturally overcomes the textit parameter inefficiency and textitlimited model accessibility for fine-tuning large target models. Our experiments across text-to-text and text-to-image demonstrate the effectiveness of our approach in mitigating toxic outputs.
  arXiv  Detail & Related papers  (2024-05-21T08:57:44Z)
• Do Membership Inference Attacks Work on Large Language Models? [141.2019867466968]
  Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model's training data. We perform a large-scale evaluation of MIAs over a suite of language models trained on the Pile, ranging from 160M to 12B parameters. We find that MIAs barely outperform random guessing for most settings across varying LLM sizes and domains.
  arXiv  Detail & Related papers  (2024-02-12T17:52:05Z)
• Practical Membership Inference Attacks against Fine-tuned Large Language Models via Self-prompt Calibration [32.15773300068426]
  Membership Inference Attacks aim to infer whether a target data record has been utilized for model training. We propose a Membership Inference Attack based on Self-calibrated Probabilistic Variation (SPV-MIA)
  arXiv  Detail & Related papers  (2023-11-10T13:55:05Z)
• Faithful Explanations of Black-box NLP Models Using LLM-generated Counterfactuals [67.64770842323966]
  Causal explanations of predictions of NLP systems are essential to ensure safety and establish trust. Existing methods often fall short of explaining model predictions effectively or efficiently. We propose two approaches for counterfactual (CF) approximation.
  arXiv  Detail & Related papers  (2023-10-01T07:31:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.