No Hidden Prompts Needed! You Can Game AI Peer Review with Presentation-Only Revisions
AuthorsXu Yang, Zhizhou Sha, Junbo Li, Jian Yu, Yifan Sun, Matthew Zhao, Jinrui Fang, Xinyue Guo, Yining Wu, Xu Hu, Yifu Luo, Qiang Liu, Zhangyang Wang
Resources
This paper shows that AI peer reviewers can be gamed by changing only how a paper is presented, not what it actually says or proves.
Key results
Cross-model adversarial repackaging success across three reviewer models
Average review score increase on a 10-point scale
Highest score gain reported in the baseline comparison subset
What the paper found
This paper, from the University of Texas at Austin, the University of Illinois Urbana-Champaign, the University of Texas at Dallas, and an independent researcher, shows that AI peer reviewers can be manipulated without hidden prompts, prompt injection, or any changes to methods, experiments, figures, equations, proofs, or numerical results. The authors introduce adversarial repackaging, a closed-loop attack that uses reviewer feedback to rewrite only presentation-level text such as the abstract, introduction, related work, discussion, and conclusion, while keeping scientific evidence fixed. Across three mainstream reviewer models—Anthropic Claude Sonnet 4, Anthropic Claude Sonnet 4.5, and OpenAI GPT-5-mini—the attack achieves a 75.1% attack success rate and a mean score gain of +1.21/10, with the strongest model-specific gain reaching +1.53 on Claude Sonnet 4. The paper further shows that narrative restructuring edits like related-work repositioning and analytical discussion expansion outperform surface polishing, revealing a strength–weakness asymmetry in which reviewers are easier to impress than to convince, and often mistake the appearance of addressing a limitation for actually resolving it. The authors release a contamination-free rolling benchmark built from over 500 recent unpublished arXiv preprints paired with LaTeX and PDF sources, and argue that resistance to presentation-only gaming is a necessary condition for safe AI-assisted peer review.
Original abstract
As AI-generated reviews move from experimental tools into peer-review infrastructure, most robustness concerns have focused on explicit attacks such as hidden instructions and prompt injection. We study a harder and more policy-relevant failure mode: no hidden text, no prompt injection, and no changes to methods, experiments, figures, equations, proofs, or numerical results. The attacker modifies only presentation-level content, such as the abstract, contribution framing, related work, discussion, and narrative structure. We introduce adversarial repackaging: a closed-loop attack that uses AI-reviewer feedback to search for presentation-level revisions while keeping the scientific evidence fixed. Across three mainstream AI reviewers, adversarial repackaging achieves a 75.1% attack success rate and a mean score gain of +1.21/10. The effect is not explained by ordinary prose polishing. We also reveal that strategies that change how the reviewer interprets the paper, such as related-work repositioning and analytical discussion expansion, substantially outperform surface edits such as local polishing, table formatting, and algorithm boxes. Our analysis reveals two deeper structural failure modes. First, AI reviewers are easier to impress than to convince: highlighting strengths reliably increases perceived merit, while attempts to dissolve weaknesses frequently backfire. Second, AI reviewers can confuse the appearance of addressing a limitation with actually resolving it, allowing unchanged evidence to be reinterpreted as stronger scientific contribution. These results show that the deployment risk is not only malicious hidden instructions, but the emergence of paper presentation itself as an optimization surface. We release a contamination-free rolling benchmark and attack framework for testing whether AI reviewers remain anchored to scientific content under presentation-only edits.
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