Hidden Secrets in the arXiv: Discovering, Analyzing, and Preventing Unintentional Information Disclosure in Source Files of Scientific Preprints

• URL: http://arxiv.org/abs/2604.20927v1
• Date: Wed, 22 Apr 2026 08:18:10 GMT
• Title: Hidden Secrets in the arXiv: Discovering, Analyzing, and Preventing Unintentional Information Disclosure in Source Files of Scientific Preprints
• Abstract summary: We analyze 2.7M arXiv submissions with available source files across three dimensions of source file-induced information disclosure.<n>Our analysis reveals that nearly every arXiv submission contains some form of "hidden" information.<n>We provide ALC-NG to comprehensively remove files, metadata, and comments that are not needed to compile a paper.
• Score: 4.620974866012306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preprints are essential for the timely and open dissemination of research. arXiv, the most widely used preprint service, takes the idea of open science one step further by not only publishing the actual preprints but also LaTeX sources and other files used to create them. As known from other contexts, such as GitHub repositories, and anecdotally exemplified for arXiv, making source code publicly available risks disclosing otherwise "hidden" information. Consequently, the public availability of paper sources raises the question of how much sensitive content is (unintentionally) disclosed through them. In this paper, we systematically answer this question for all 2.7M arXiv submissions with available source files across three dimensions of source file-induced information disclosure: (1) inclusion of unnecessary files, (2) metadata embedded in files, and (3) irrelevant content in files such as source code comments. Our analysis reveals that nearly every arXiv submission contains some form of "hidden" information. Notable findings range from links to editable web documents for internal coordination over API and private keys to complete Git histories. While different tools promise to remove such information from source files, we show that they fail to reliably achieve the intended cleaning functionality. To mitigate this situation, we provide ALC-NG to comprehensively remove files, metadata, and comments that are not needed to compile a LaTeX paper.

Related papers

• Easy Reads: A Python program for making Scientific Papers on arXiv more Reader Friendly and Accessible [0.0]
  Easy Reads is an automated, end-to-end, open-source Python program that makes papers from arXiv more reader-friendly and accessible.<n>The main goal of Easy Reads is to facilitate ease of reading of scientific papers.
  arXiv  Detail & Related papers  (2026-06-18T17:57:26Z)
• AI-Friendly LaTeX: Using LaTeX Code as a Knowledge Source for Retrieval-Augmented Generation [0.0]
  Large language models can answer questions about textbooks, lecture notes, and programming exercises more reliably when their answers are grounded in an explicit knowledge source.<n>For mathematical and technical material, the original source can be a better starting point than a PDF, because it contains structural information, labels, sectioning commands, macros, and authorial intent that are often lost or distorted in PDF extraction.<n>This article describes a focused preprocessing approach for turning source, together with its compiled auxiliary files and optional author annotations, into chunks suitable for indexing in a vector database.
  arXiv  Detail & Related papers  (2026-05-21T18:01:51Z)
• Scaling Accessible Mathematics on arXiv: HTML Conversion and MathML 4 [0.0]
  We report on the ongoing development of arXiv's HTML Papers offering, available on every new La submission since its initial release in 2023.<n>The main highlights from 2025 and early 2026 are: (i) community-driven improvements to HTML fidelity and service health, with roughly half of 6,000 user reports resolved.<n>An in-progress Rust port of HTMLML, reducing compute costs and enabling faster previews on submission.
  arXiv  Detail & Related papers  (2026-05-15T19:04:45Z)
• Paper Espresso: From Paper Overload to Research Insight [53.45135239997754]
  We present Paper Espresso, an open-source platform that automatically discovers, summarizes, and analyzes trending arXiv papers.<n>The system uses large language models (LLMs) to generate structured summaries with topical labels and keywords.<n>Over 35 months of continuous deployment, Paper Espresso has processed over 13,300 papers and publicly released all structured metadata.
  arXiv  Detail & Related papers  (2026-04-06T09:45:21Z)
• You Have Been LaTeXpOsEd: A Systematic Analysis of Information Leakage in Preprint Archives Using Large Language Models [1.0268444449457959]
  In the absence of sanitization, submissions may disclose sensitive information that adversaries can harvest using open-source intelligence.<n>We present the first large-scale security audit of preprint archives, analyzing more than 1.2 TB of source data from 100,000 arXiv submissions.<n>We urge the research community and repository operators to take immediate action to close these hidden security gaps.
  arXiv  Detail & Related papers  (2025-10-04T10:03:17Z)
• Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models [44.31597857713689]
  We introduce Active Indexing for the first stage, which creates generalizable, source-anchored bindings.<n>Experiments with Qwen-2.5-7B&3B show that Active Indexing consistently outperforms a Passive Indexing baseline.<n> internal citations complement external ones by making the model more robust to retrieval noise.
  arXiv  Detail & Related papers  (2025-06-21T04:48:05Z)
• GeAR: Generation Augmented Retrieval [82.20696567697016]
  This paper introduces a novel method, $textbfGe$neration.<n>It improves the global document-Query similarity through contrastive learning, but also integrates well-designed fusion and decoding modules.<n>When used as a retriever, GeAR does not incur any additional computational cost over bi-encoders.
  arXiv  Detail & Related papers  (2025-01-06T05:29:00Z)
• FAMuS: Frames Across Multiple Sources [74.03795560933612]
  FAMuS is a new corpus of Wikipedia passages that emphreport on some event, paired with underlying, genre-diverse (non-Wikipedia) emphsource articles for the same event. We present results on two key event understanding tasks enabled by FAMuS.
  arXiv  Detail & Related papers  (2023-11-09T18:57:39Z)
• How many preprints have actually been printed and why: a case study of computer science preprints on arXiv [9.783989953810725]
  We quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code.
  arXiv  Detail & Related papers  (2023-08-03T17:56:16Z)
• ThreatCrawl: A BERT-based Focused Crawler for the Cybersecurity Domain [0.0]
  This paper proposes a new focused crawler called ThreatCrawl.<n>It uses BiBERT-based models to classify documents and adapt its crawling path dynamically.<n>It yields harvest rates of up to 52%, which are, to the best of our knowledge, better than the current state of the art.
  arXiv  Detail & Related papers  (2023-04-24T09:53:33Z)
• unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network [0.0]
  We propose a new version of the data set unarXive. The resulting data set comprises 1.9 M publications spanning multiple disciplines and 32 years. In addition to the data set, we provide ready-to-use training/test data for citation recommendation and IMRaD classification.
  arXiv  Detail & Related papers  (2023-03-27T07:40:59Z)
• The Semantic Scholar Open Data Platform [92.2948743167744]
  Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature.<n>We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction.<n>The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings.
  arXiv  Detail & Related papers  (2023-01-24T17:13:08Z)
• Cracking Double-Blind Review: Authorship Attribution with Deep Learning [43.483063713471935]
  We propose a transformer-based, neural-network architecture to attribute an anonymous manuscript to an author. We leverage all research papers publicly available on arXiv amounting to over 2 million manuscripts. Our method achieves an unprecedented authorship attribution accuracy, where up to 73% of papers are attributed correctly.
  arXiv  Detail & Related papers  (2022-11-14T15:50:24Z)
• CitationIE: Leveraging the Citation Graph for Scientific Information Extraction [89.33938657493765]
  We use the citation graph of referential links between citing and cited papers. We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
  arXiv  Detail & Related papers  (2021-06-03T03:00:12Z)
• ArXiving Before Submission Helps Everyone [38.09600429721343]
  We analyze the pros and cons of arXiving papers. We see no reasons why anyone but the authors should decide whether to arXiv or not.
  arXiv  Detail & Related papers  (2020-10-11T22:26:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.