Scientists decry conference's use of hidden prompts to snare AI peer reviews

Organizers of the NeurIPS 2026 conference added hidden prompts to papers to catch peer reviewers using generative AI, sparking backlash from researchers who say the tactic erodes trust. The conference bans AI chatbots for reviewing but allows background use; some reviewers have already been caught, and similar efforts at ICML 2026 have flagged hundreds of violations.

Organizers of a prominent neuroscience conference are facing pushback on social media after adding hidden prompts to their papers to catch peer reviewers who are using generative artificial intelligence AI to referee papers. The 40th Annual Conference on Neural Information Processing Systems NeurIPS https://neurips.cc/ —which is slated to take place in Sydney, Australia, in December 2026—bans peer reviewers from uploading papers they referee to AI chatbots, as the practice breaches confidentiality. Reviewers can still use AI chatbots for background research purposes, according to the policy outlined in the conference’s handbook https://neurips.cc/Conferences/2026/MainTrackHandbook . To enforce the policy and catch illicit AI use in peer review, the event’s organizers have included deliberately concealed instructions for large language models LLMs in papers sent out for peer review. The instructions tell an LLM to use telltale phrases—such as “This work addresses the central challenge” and “The claims of the paper”—in a peer-review report. Some researchers have already been caught https://doi.org/10.1038/d41586-025-02172-y trying to sneak secret messages into their papers in a bid to game AI tools into giving them favorable referee reports. Many publishers ban the use of AI in peer review https://doi.org/10.1038/d41586-025-00894-7 . Multiple researchers https://www.linkedin.com/posts/saraatito neurips-peerreview-llm-share-7475127189148139521-hZSj/ refereeing papers for NeurIPS have taken to social media https://www.linkedin.com/posts/soerenauer peerreview-neurips-researchintegrity-share-7475256943901700096-xkIO/?utm source=share&utm medium=member desktop&rcm=ACoAAAtckgcBDCaCWrdZUUaUhB9ImaMkwbEsGtI to express their concerns https://www.linkedin.com/posts/saman-forouzandeh-7b530569 peerreview-researchintegrity-machinelearning-share-7472546402984701952-Dm6Y/ about the indirect prompt injections inserted into papers. “Designing a trap that presumes bad faith corrodes the relationship the whole system depends on,” Sören Auer https://www.tib.eu/de/forschung-entwicklung/forschungsgruppen-und-labs/data-science-and-digital-libraries/mitarbeiterinnen-und-mitarbeiter/soeren-auer , a computer scientist at Leibniz University Hannover, wrote on LinkedIn. “You do not build a healthy reviewing culture by treating your reviewers as suspects.” But others see merits in the approach. A similar prompt-injection effort has caught hundreds of reviewers misusing LLMs in submissions for next week’s 43 rd International Conference on Machine Learning https://openreview.net/challenge?redirect=%2Fgroup%3Fid%3DICML.cc%2F2026%2FConference ICML 2026 in Seoul, South Korea, according to Nihar Shah https://www.cs.cmu.edu/~nihars/ , a computer scientist at Carnegie Mellon University and scientific integrity chair of that conference. In a statement to The Transmitter , the NeurIPS organizing committee says it can’t discuss injected prompts in detail “without eroding the effectiveness of this intervention.” A The Transmitter he was assigned eight NeurIPS papers to review. He says he sometimes converts PDF files into Microsoft Word documents when carrying out peer review, which renders some prompts visible. Auer says he initially rejected the first paper he was reviewing because he thought the prompts had been inserted by the study’s authors. But he removed the flag after discovering hidden prompts in a second paper and seeing researchers discussing this issue on a Reddit thread. It’s possible that more papers are being rejected because referees don’t know that prompts were inserted by conference organizers, he says. “I personally think it’s not good to prohibit the use of AI,” Auer adds. “We should rather, of course, have a discussion on how to use it.” The NeurIPS committee has been replying directly to any reviewer who has noticed the hidden prompts, informing them not to penalize individual papers, according to the statement. Like Auer, Sara Atito https://www.surrey.ac.uk/people/sara-atito , an AI researcher at the University of Surrey, told The Transmitter she spotted the same prompt in all four papers she reviewed for NeurIPS. She says she also found it in the version of her own paper that NeurIPS organizers created before sending the paper out for peer review. Atito calls hidden prompts a “poor mechanism,” arguing that it may filter out some problematic submissions but won’t solve the bigger problems with peer review. “We put too much blame on reviewers because they are the visible point of failure,” she says. But Shah says hidden prompts are “viable and feasible.” Shah led a similar effort https://blog.icml.cc/2026/03/18/on-violations-of-llm-review-policies/ at ICML 2026 by injecting hidden prompts into all submitted papers. By doing so, Shah says, he and his team identified hundreds of referees who were using AI when they weren’t supposed to, leading to their reviews being rejected. ICML 2026 desk-rejected just under 500 papers https://aiweekly.co/alerts/icml-desk-rejects-497-papers-over-llm-review-violations over violations of its LLM review policy—about 2 percent of the total number of submissions the conference received this year. Researchers expressed “overwhelming support” for the strategy, says Shah, who adds that he shared the methodology with the NeurIPS team. “I have been working on conference peer review for several years, and I have hardly seen such strong support for anything,” he says. “People were really tired of reviewers copy-pasting AI-generated reviews without putting any effort.”