Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915989610823680 |
|---|---|
| author | Chen, Zeyuan Ma, Yihan Shen, Xinyue Backes, Michael Zhang, Yang |
| author_facet | Chen, Zeyuan Ma, Yihan Shen, Xinyue Backes, Michael Zhang, Yang |
| contents | Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data raises serious privacy concerns. We introduce the PopQuiz Attack, a black-box membership inference attack that tests whether a model can recall specific training examples. The core idea is to turn target data into quiz-style multiple-choice questions and infer membership from the model's answers. Across six widely used LLMs (GPT-3.5, GPT-4o, LLaMA2-7b, LLaMA2-13b, Mistral-7b, and Vicuna-7b) and four datasets, our method achieves an average ROC-AUC of 0.873 and outperforms existing approaches by 20.6%. We further analyze factors affecting attack success, including query complexity, data type, data structure, and training settings. We also evaluate instruction-based, filter-based, and differential privacy-based defenses, which reduce performance but do not eliminate the risk. Our results highlight persistent privacy vulnerabilities in modern LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06423 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models Chen, Zeyuan Ma, Yihan Shen, Xinyue Backes, Michael Zhang, Yang Cryptography and Security Large language models (LLMs) show strong performance across many applications, but their ability to memorize and potentially reveal training data raises serious privacy concerns. We introduce the PopQuiz Attack, a black-box membership inference attack that tests whether a model can recall specific training examples. The core idea is to turn target data into quiz-style multiple-choice questions and infer membership from the model's answers. Across six widely used LLMs (GPT-3.5, GPT-4o, LLaMA2-7b, LLaMA2-13b, Mistral-7b, and Vicuna-7b) and four datasets, our method achieves an average ROC-AUC of 0.873 and outperforms existing approaches by 20.6%. We further analyze factors affecting attack success, including query complexity, data type, data structure, and training settings. We also evaluate instruction-based, filter-based, and differential privacy-based defenses, which reduce performance but do not eliminate the risk. Our results highlight persistent privacy vulnerabilities in modern LLMs. |
| title | Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2605.06423 |