ArticleResearch integrity and peer review2026
Prompt injection in manuscripts: exploiting loopholes or crossing ethical lines?
Article in Research integrity and peer review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Disclosure is not documentation: an open science framework for documenting generative AI use in scholarly research and publication workflows.Research integrity and peer review · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe integration of AI in academic publishing has raised significant ethical concerns, particularly regarding the practice of prompt injection, where hidden instructions are embedded in manuscripts to manipulate AI responses in the peer review process.
methodsThis study employed a mixed-methods approach, combining a comprehensive content analysis of academic integrity guidelines with a survey of 194 stakeholders, including authors, peer reviewers, and journal editors from various academic fields. The survey focused on their awareness of prompt injection, perceptions of its ethical implications, and views on AI transparency in peer review.
resultsThe findings reveal that a substantial proportion of participants (80%) support greater transparency in the use of AI in peer review. Many respondents reported frustrations with the inconsistencies and inefficacies of AI-generated feedback, prompting some to consider the use of prompt injection as a strategy to secure favorable review outcomes. Importantly, the analysis identified a significant gap in current definitions of research misconduct, which do not adequately address the ethical implications of AI interventions.
conclusionsThis study highlights the urgent need for revised ethical frameworks that incorporate AI-related issues in academic publishing, advocating for policies that promote transparency and uphold the integrity of the peer review process.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.