The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Hidden Prompts in Manuscripts Exploit AI-Assisted Peer Review
Open accessAbstract
Highlighting the need for controlled AI integration in peer review and harmonized policies governing AI use in academic evaluation.
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Citations
Cites
Hosseini, M., & Horbach, S. P. J. M. (2023) · Research Integrity and Peer Review · doi:10.1186/s41073-023-00133-5 · Cited by 328 · cited 1×
“3 This burden creates incentives for shortcuts that compromise review integrity.”
Use of Artificial Intelligence in Peer Review Among Top 100 Medical Journals
Li, Z.-Q., Xu, H.-L., Cao, H.-J., Liu, Z.-L., Fei, Y.-T., & Liu, J.-P. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.48609 · Cited by 80
Flagged first, then by citations
Research works printed but not linked: 8
- Choi B. et al. Invisible text injection: The Trojan horse of AI-assisted medical peer review. medRxiv (July 2025); 2025.07.24.25332148 · DOI
- Collu M.G. et al. Publish to perish: Prompt injection attacks on LLM-assisted peer review. arXiv (Aug. 2025).
- 10.1016/j.tics.2023.12.002 · DOI
- Lin, Z. We need to rein in the AI gatekeepers of science now. Nature 645, 286 (Sept. 2025).
- Murray T. PhantomLint: Principled detection of hidden LLM prompts in structured documents. arXiv (Oct. 2025).
- Rao V. Kumar A. Lakkaraju H. and Shah N.B. Detecting LLM-written peer reviews. arXiv (Oct. 2025). · DOI · Paper page
- Sugiyama S. and Eguchi R. ‘Positive review only’: Researchers hide AI prompts in papers. Nikkei Asia (2025).
- Ye R. et al. Are we there yet? Revealing the risks of utilizing large language models in scholarly peer review. arXiv (Dec. 2024).
Cited by
Sorted by citations
AI-Assisted Peer Review Across Research Communities: From Reviewer AI Policies to LLM Review Quality
Fichtl, A. M., Ellinger, L., Kelber, J., Olík, K., & Groh, G. (2026) · arXiv · doi:10.48550/arxiv.2608.03581
“Prior work has also identified limitations of AI-generated reviews, including a tendency to overrate borderline papers (Russo Latona et al., 2025) and vulnerability to hidden instructions in submissions (Lin, 2026).”
Metadata
- Authors
- Lin, Z.
- Affiliations
- Yonsei University (KR)
- Corresponding author
- Lin, Zhicheng
- ORCID
- Lin, Zhicheng
- Venue
- Communications of the ACM
- Published
- 2026-06-30
- Volume
- 69
- Issue
- 7
- Pages
- 53-56
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W4412106961
- Cited by
- 7
- References
- 2
- Citations by year
- 2026: 6
- FWCI
- 50.90
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- PsyArXiv (OSF Preprints) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Key R&D Program of China STI2030 Major Projects (2021ZD0204200)
- Yonsei University Research Fund (5355661)
- Topics
- Social Sciences › Library and Information Sciences › Publishing and Scholarly Communication
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Library and Information Sciences › Academic Publishing and Open Access
- Keywords
- AI-assisted peer review, AI-generated text, AI detection, academic integrity, AI policy
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