Strong Membership Inference Attacks on Massive Datasets and (Moderately) Large Language Models (2505.18773v1)

Abstract: State-of-the-art membership inference attacks (MIAs) typically require training many reference models, making it difficult to scale these attacks to large pre-trained LLMs. As a result, prior research has either relied on weaker attacks that avoid training reference models (e.g., fine-tuning attacks), or on stronger attacks applied to small-scale models and datasets. However, weaker attacks have been shown to be brittle - achieving close-to-arbitrary success - and insights from strong attacks in simplified settings do not translate to today's LLMs. These challenges have prompted an important question: are the limitations observed in prior work due to attack design choices, or are MIAs fundamentally ineffective on LLMs? We address this question by scaling LiRA - one of the strongest MIAs - to GPT-2 architectures ranging from 10M to 1B parameters, training reference models on over 20B tokens from the C4 dataset. Our results advance the understanding of MIAs on LLMs in three key ways: (1) strong MIAs can succeed on pre-trained LLMs; (2) their effectiveness, however, remains limited (e.g., AUC<0.7) in practical settings; and, (3) the relationship between MIA success and related privacy metrics is not as straightforward as prior work has suggested.

• The paper shows strong membership inference attacks can exceed random baselines on large language models but are limited in practical performance.
• Real-world attack success on LLMs trained optimally is limited, with AUC below 0.7, indicating resilience to current strong attacks.
• Membership inference attack vulnerability does not consistently correlate with data extraction risks, showing a complex relationship with privacy metrics.

Analyzing Strong Membership Inference Attacks on LLMs

The paper presented investigates the efficacy and constraints of strong Membership Inference Attacks (MIAs) on LLMs, focusing on models of significantly larger scales and datasets than previously studied. This research contributes critical insights into privacy risks associated with LLMs and examines whether stronger attacks using numerous reference models can effectively determine the membership of data, thus revealing potential privacy vulnerabilities.

Key Findings

The paper articulates three pivotal findings concerning the applicability and performance of MIAs on pre-trained LLMs:

1. Strong MIAs Potentially Effective: The analysis demonstrates that sophisticated MIAs can indeed achieve success on LLMs under proper configurations. By scaling LiRA—a notably effective MIA strategy—across GPT-2 models ranging from 10 million to 1 billion parameters, it has been observed that these attacks surpass random prediction baselines significantly. Yet, the performance extends only to a certain threshold, indicating fundamental constraints in attack effectiveness in practical scenarios.
2. Limited Attack Success in Practical Conditions: While MIAs prove feasible on LLMs, their real-world success is limited to an $\mathrm{AUC}$ below 0.7 within typical training configurations. This shows that LLMs, when trained according to computing-optimal practices (e.g., adhering to Chinchilla Scaling Laws), exhibit a resistance level that keeps MIAs' success at a moderately low performance ceiling. Hence, the efficacy of MIAs remains bounded even when accounting for intensive computational resources and optimal model conditions in standard training routines.
3. Complex Relationship with Privacy Metrics: The correlation between MIA vulnerability and related privacy metrics, particularly concerning data extraction risks, is not straightforward. The research reveals that while MIAs capture some memorization signals, they do not necessarily align with conditions under which data extraction succeeds—despite some previous assumptions. The vulnerability trend showcases interesting complexities, such as increased risk with longer sample lengths and recency in training, yet overall classifies memorization risks differently from those predicted by extraction-focused evaluations.

Methodological Advances

• Scale and Scope: This paper is novel in its execution of MIAs on LLMs at a truly large scale involving approximately 20 billion tokens from the C4 dataset. Such a substantial dataset enables more reliable assessments of MIA performance across varying model architectures and training datasets, providing richer benchmarks for evaluating privacy risks.
• Reference Model Utilization: By employing over 4,000 reference models, the paper leverages both breadth and depth in its analysis of MIAs. The research thoroughly investigates the impact of using a widespread base of reference models on attack effectiveness, offering substantial evidence that increased computational investments yield marginal gains in effectiveness beyond specific points.

Implications and Future Directions

The findings indicate that while strong MIAs indicate some decline in privacy within LLMs, the extent of risk remains nuanced and context-dependent. Practically, the demonstrated limits of these attacks underline the resilience of LLMs to such privacy threats when trained optimally, implying that while AI models can be improved for security, more sophisticated or varied attack methodologies may be necessary to fully assess and understand privacy risks.

On the theoretical side, the implications of this work challenge researchers to develop better models for understanding which facets of model training influence memorization and privacy risks most significantly. Future research might further explore how different architectures, training regimes, or augmented data handling practices can mitigate risks without hindering model performance. Additionally, new benchmarks and standards for MIA evaluation could enhance the rigorous paper of privacy in machine learning.

This paper provides valuable foundations for both security experts and model developers focused on enhancing LLM privacy. By clarifying the actual potential and limits of MIAs in real-world applications, it opens pathways for advancing both AI model security measures and the theoretical framework underpinning privacy evaluation in machine learning.