paperMay 2026Unreviewed

Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models

Zeyuan Chen, Yihan Ma, Xinyue Shen, Michael Backes, Yang Zhang

Abstract

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,

Framework mappings

OWASP Top 10 for LLM Applications

LLM02Sensitive Information Disclosure

MITRE ATLAS

AML.T0024.000Infer Training Data Membership

Suggested from the entry's categories.

Cite

@misc{chen2026pop,
  title = {{Pop Quiz Attack: Black-box Membership Inference Attacks Against Large Language Models}},
  author = {Zeyuan Chen and Yihan Ma and Xinyue Shen and Michael Backes and Yang Zhang},
  year = {2026},
  month = may,
  eprint = {2605.06423},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.06423}
}