You Have Been LaTeXpOsEd: A Systematic Analysis of Information Leakage in Preprint Archives Using Large Language Models
LLM analysis of arXiv LaTeX source reveals systematic information leakage patterns beyond published PDFs, exposing unintended data disclosure in academic publishing.
Summary written by editorial AI · Source link below
arXiv:2510.03761v2 Announce Type: replace Abstract: The widespread use of preprint repositories such as arXiv has accelerated the communication of scientific results but also introduced overlooked security risks. Beyond PDFs, these platforms provide unrestricted access to original source materials, including LaTeX sources, auxiliary code, figures, and embedded comments. In the absence of sanitization, submissions may disclose sensitive information that adversaries can harvest using open-source
External link — opens at arXiv Crypto & Security in a new tab.
More from the Research Desk
- Signed, Sealed, and Relayed, NTLM-style: Bypassing LDAP Signing Through Reauthentication3d
- Not Where You Think: Protecting Location Privacy Against Reasoning-Based Geolocation4d
- Social-HUMINT: An LLM-Augmented Unified Framework for Measuring Social Media Exposure4d
- Practical Auditing of Differentially Private Graph Release Mechanisms: Methodology, Toolkit, and Case Study4d
- SecLog-Bench: A Scalable Benchmark for Cybersecurity Log Reasoning with Scaffolded Evidence4d