Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models (2411.00154v1)

Abstract: Membership inference attacks (MIA) attempt to verify the membership of a given data sample in the training set for a model. MIA has become relevant in recent years, following the rapid development of LLMs (LLM). Many are concerned about the usage of copyrighted materials for training them and call for methods for detecting such usage. However, recent research has largely concluded that current MIA methods do not work on LLMs. Even when they seem to work, it is usually because of the ill-designed experimental setup where other shortcut features enable "cheating." In this work, we argue that MIA still works on LLMs, but only when multiple documents are presented for testing. We construct new benchmarks that measure the MIA performances at a continuous scale of data samples, from sentences (n-grams) to a collection of documents (multiple chunks of tokens). To validate the efficacy of current MIA approaches at greater scales, we adapt a recent work on Dataset Inference (DI) for the task of binary membership detection that aggregates paragraph-level MIA features to enable MIA at document and collection of documents level. This baseline achieves the first successful MIA on pre-trained and fine-tuned LLMs.

Overview of "Scaling Up Membership Inference: When and How Attacks Succeed on LLMs"

The paper "Scaling Up Membership Inference: When and How Attacks Succeed on LLMs" explores the efficacy of Membership Inference Attacks (MIA) on LLMs. This work investigates the conditions under which MIAs are successful, addressing a critical gap in the understanding of privacy concerns related to the utilization of copyrighted data in LLMs.

Key Contributions

The paper makes several notable contributions to the field of machine learning privacy:

1. Novel Evaluation Protocol: The authors introduce comprehensive evaluation benchmarks for MIA applied to various text scales, ranging from sentences to collections of documents. This provides a structured approach for analyzing the performance of MIAs in relation to the size and complexity of the input data.
2. Aggregation-Based MIA Paradigm: Extending upon previous research, the paper implements dataset inference techniques to adapt MIAs for different textual granularities. The aggregation technique developed by Maini et al. (2024) is enhanced to support a more detailed analysis across scales, emphasizing particularly long token sequences.
3. The Role of Text Scales in MIA Success: By investigating four data scales—sentence, paragraph, document, and collection—the paper elucidates that MIAs substantially improve when applied to larger scales. In some instances, particularly at the document and collection levels, the reported AUROC scores significantly exceed 80%, validating the hypothesis that small text units might obfuscate detection capabilities.
4. Implications for Fine-Tuning and Continual Learning: The findings suggest that fine-tuning LLMs, especially within smaller datasets, increases susceptibility to MIAs. This insight provides a foundation for developing strategies to counterbalance potential vulnerabilities introduced during specific stages of model training.

Experimental Outcomes and Results

The experiments highlight several important findings:

• Text Length and MIA Performance: The research demonstrates that MIA approaches show substantial performance improvements when applied to sequences of 10K tokens or more. Paragraph-level performance needs to surpass random chance significantly to contribute effectively to document and collection MIA success, revealing a compounded effect with extended text.
• Impact of Training Scenarios: By examining MIA across different LLM training stages, including continual learning and fine-tuning, the authors reveal that models updated with continual learning cycles remain resilient to sentence-level MIAs. However, fine-tuned models, which often engage with smaller datasets, are more vulnerable to membership inference.
• Effect of Paragraph Aggregation: The paper underscores a profound compounding effect where slight gains in paragraph-level AUROC yield dramatic improvements at the document and collection levels, emphasizing the necessity for strategically aggregating membership signals to protect data privacy.

Theoretical and Practical Implications

The research provides crucial insights into the theoretical mechanics of MIAs, particularly as they pertain to LLMs and copyright infringement. Practically, it advances the discourse on privacy-preserving measures needed for deploying LLMs, advocating for robust training methodologies sensitive to membership leakage.

The proposed benchmarks and novel methodologies could serve as a foundation for future AI developments, offering a quantifiable means to evaluate MIA risk across diverse contexts. As MIA practices evolve, this paper's insights on scalability may direct further investigations into optimizing privacy without undermining model efficacy.

Speculation on Future Developments

Looking ahead, the paper prompts further exploration into the development of LLM architectures inherently resilient to MIAs across all scales. Future research might focus on integrating privacy-preserving techniques or enhanced model architectures that address emerging vulnerabilities identified within this work. Additionally, as the field progresses, incorporating broader baselines for MIA methodologies could potentially improve detection rates and shape adaptive privacy frameworks for LLM-based applications.