Detecting Verbatim LLM Copy-Paste in Homework

2026-05-07

arXiv.org

AI Summary

This work addresses the academic integrity issue of students directly pasting assignment prompts into large language models (LLMs) and submitting verbatim outputs as their own work. To counter this, the authors propose an educator-controlled, input-side invisible watermarking technique that embeds imperceptible instructions within visible prompts using Unicode Tags (U+E0000–U+E007F). When a student copies such a prompt verbatim into an LLM, the model automatically generates a detectable signature in its response—requiring no cooperation from model providers and avoiding false positives for non-native speakers. The accompanying zero-dependency, single-page tool, SteganoPrompt, integrates seamlessly with mainstream document editors and learning platforms. Evaluations across eight LLM families and diverse educational contexts demonstrate the watermark’s robustness and compliance with ethical standards. The open-source implementation enables transparent and responsible plagiarism detection.

Abstract

Large language models (LLMs) have made fluent essay writing, code drafting, and quiz answering instantly available to students at every level, from secondary school through graduate study. Many educators do not object to LLM use \emph{per~se}; what they need to detect is the case in which a student pastes the assignment prompt into a chatbot and submits the model's reply verbatim, without engaging with the work. Existing post-hoc AI-text detectors remain unreliable and have been shown to penalise non-native English writers, while output-side watermarks require cooperation from the model provider. We propose an alternative that the educator controls directly: an input-side watermark in which an invisible instruction is embedded inside the visible assignment prompt itself. An LLM that ingests the prompt verbatim quietly reads the hidden instruction and writes a tell-tale signature into its reply, exposing the copy-and-paste pathway specifically. We describe SteganoPrompt, a single-page, zero-dependency web tool that encodes an arbitrary printable-ASCII payload into the deprecated Unicode Tags block (\texttt{U+E0000}--\texttt{U+E007F}). The encoded string is visually identical to the original, survives common copy-paste channels (Word, Google Docs, PDF, Markdown, Slack, e-mail, the major learning-management systems), and is reliably tokenized by frontier models. We evaluate compliance across seven LLM families and a representative set of educational content channels. The work is informed by my experience as a graduate teaching assistant for an undergraduate software engineering course at the George Washington University. The tool is released under the MIT licence at \url{https://ezharjan.github.io/SteganoPrompt/}.

Problem

• watermarking
• LLM detection
• academic integrity
• verbatim copying
• input-side watermark

Innovation

• input-side watermark
• SteganoPrompt
• LLM detection
• Unicode steganography
• academic integrity