Do Membership Inference Attacks Work on Large Language Models? (2402.07841v2)

Abstract: Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model's training data. Despite extensive research on traditional machine learning models, there has been limited work studying MIA on the pre-training data of LLMs. We perform a large-scale evaluation of MIAs over a suite of LLMs (LMs) 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. Our further analyses reveal that this poor performance can be attributed to (1) the combination of a large dataset and few training iterations, and (2) an inherently fuzzy boundary between members and non-members. We identify specific settings where LLMs have been shown to be vulnerable to membership inference and show that the apparent success in such settings can be attributed to a distribution shift, such as when members and non-members are drawn from the seemingly identical domain but with different temporal ranges. We release our code and data as a unified benchmark package that includes all existing MIAs, supporting future work.

• The paper shows that membership inference attacks perform near random levels on LLMs, indicating minimal risk of membership leakage under standard training settings.
• The paper demonstrates that extensive training datasets and limited iterations obscure clear membership distinctions, challenging traditional attack methods.
• The paper finds that variations in model size and data deduplication further reduce inference effectiveness, suggesting a need for novel attack frameworks.

Membership Inference Attacks on LLMs: Evaluation and Challenges

The paper "Do Membership Inference Attacks Work on LLMs?" presents an extensive paper investigating the effectiveness of membership inference attacks (MIAs) on LLMs. MIAs attempt to discern if a specific data point was part of a model's training data, a critical question for understanding the privacy implications of machine learning models. While substantial research has been conducted on more traditional models, the application and efficacy of MIAs on LLMs, particularly during the pre-training phase, remains insufficiently explored. This paper aims to address this gap by evaluating various MIAs on LLMs, with parameters ranging from 160 million to 12 billion, trained on the Pile dataset.

Key Findings

1. Limited Attack Efficacy: The paper reveals that MIAs largely perform at near-random levels across several domains and model sizes, with minor exceptions. This indicates that, under typical settings, MIAs may not effectively expose membership information in LLMs.
2. Influence of Training Characteristics: The paper attributes the lack of MIA success to two primary factors: the vast size of the training datasets combined with limited training iterations, and a fuzzy boundary between members and non-members, exacerbated by natural data redundancies and overlaps.
3. Impact of Model Size and Training Protocols: While increasing model size showed marginal improvements in MIA effectiveness, deduplication of training data led to slight decreases in performance. Interestingly, both large datasets and near-single epoch training appear to diminish the memorization of training data, counteracting MIA efficacy.
4. Distribution Shifts: Specific experimental setups demonstrated vulnerabilities, particularly when members and non-members came from similar domains but different temporal frames, highlighting the role of distribution shifts in enhancing attack success. Such shifts could provide misleading results in MIA assessments.
5. Benchmark and Evaluation: The authors release their benchmarking code and data, facilitating further research and development of MIAs in the context of LLMs.

Implications and Future Work

The findings suggest that existing MIAs face considerable challenges in effectively operating against pre-trained LLMs primarily due to their training configurations and the inherent ambiguity of language data. The implications are significant both theoretically and practically, pointing towards the need for developing new attack techniques or reconsidering the definition of membership in generative models.

Future research could explore:

• Enhanced Attack Frameworks: Developing more sophisticated attacks that can exploit subtler learning signals or potential memorization artifacts in LLMs.
• Broader Model Evaluation: Assessing models beyond LLMs, including domain-specific or fine-tuned models that may have different training characteristics.
• Revisiting the Privacy Paradigm: With LLMs, the nuance of data overlaps and semantic similarities might necessitate a revised interpretation of what constitutes data leakage or memorization, possibly integrating semantic metrics.
• Analysis of Data Characteristics: Expanding the understanding of how data diversity and domain-specific traits impact MIA performance.

Overall, the paper underscores the complex interplay between model training dynamics and the privacy vulnerabilities exploited by MIAs, prompting a call for re-evaluation of both attack methodologies and privacy safeguards in the era of LLMs.