arXiv cs.CL· Aizierjiang Aiersilan, Artin Yousefi, Robert Pless·· 11 小时前AI 评分47
SteganoPrompt：在作业提示词中植入水印以检测 LLM 逐字复制粘贴的回答
On Seeding Watermarks to Detect Verbatim LLM Copy-Paste Responses
AI 导读
研究者提出 SteganoPrompt，一种由教师直接控制的输入侧水印方案：把隐形指令嵌入可见的作业提示词中，LLM 逐字读取后会按指令在回答里留下可识别签名，从而暴露复制粘贴路径。
正文 · 原文
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 tokenized as ordinary text by most frontier models. We evaluate compliance across eight LLM families and a representative set of educational content channels. To keep deployment transparent, disclosure is built into the tool itself: every encoded brief also carries a brief, always-on academic-integrity reminder for the student, and we set out concrete guidance recommending for its disclosed, ethical use in the classroom. The tool is released under the MIT licence at this https URL.
| Comments: | We are pleased to announce that this paper has been accepted by the 9th AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026). We appreciate the valuable feedback from the reviewers and look forward to sharing our findings with the community |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Emerging Technologies (cs.ET) |
| Cite as: | arXiv:2605.16336 [cs.CR] |
| (or arXiv:2605.16336v2 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16336 arXiv-issued DOI via DataCite |
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| Journal reference: | Proceedings of the Ninth AAAI/ACM Conference on AI, Ethics, and Society (AIES 2026) |
| Related DOI: | https://doi.org/10.1609/aies.v9i1.43126
DOI(s) linking to related resources |
Submission history
From: Aizierjiang Aiersilan [view email]
[v1]
Thu, 7 May 2026 02:36:27 UTC (334 KB)
[v2]
Fri, 7 Aug 2026 04:08:34 UTC (235 KB)
来源：arXiv cs.CL · arxiv.org