Richard A. Dubniczky and Bertalan Borsos, Eötvös Loránd University; Tamas Bisztray, HUN-REN Sztaki; Norbert Tihanyi, Technology Innovation Institute
In this work, we present the first large-scale security audit of the arXiv preprint repository, analyzing over 1.2 TB of data from 100,000 arXiv submissions to report on systemic sensitive information leakage. When authors upload submissions, they publish not only a PDF but also auxiliary code, images, and LaTeX source files containing embedded comments. In the absence of sanitization, these files often disclose sensitive information that adversaries can harvest using open-source intelligence. Operating under a strict ethical framework of passive verification, we introduce LaTeXpOsEd, a pipeline that integrates pattern matching, logical filtering, and large language models (LLMs) to detect context-dependent secrets within LaTeX comments and unreferenced auxiliary files. To evaluate the secret-detection capability of LLMs, we introduce LLMSec-DB, a benchmark on which we tested 25 state-of-the-art models. Analyzing publicly available arXiv submissions, we uncover thousands of PII exposures, hundreds of instances of exposed credentials, private Google Drive links, API keys, and various semantic leaks, including internal disputes and confidential peer reviews. We show that this large-scale extraction of sensitive information is economically viable for low-resource adversaries leveraging open-weight models and constitutes a serious security and reputational threat to individuals and institutions. We urge the research community and repository operators to take immediate action to close these hidden security gaps. To support open science and in accordance with responsible disclosure standards, we have published our toolset and benchmarks on GitHub and Zenodo.
