The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks
Open accessAbstract
Tzu-Ling Lin, Wei-Chih Chen, Teng-Fang Hsiao, Hou-I Liu, Ya-Hsin Yeh, Yu-Kai Chan, Wen-Sheng Lien, Po-Yen Kuo, Philip S. Yu, Hong-Han Shuai. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.
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“Their evaluations can also be influenced by manuscript-level factors, including hidden prompt injection, adversarial textual perturbations, metadata-related biases, and presentation-related variation (Ye et al., 2024; Collu et al., 2026; Lin et al., 2025; Vasu et al., 2026).”
From How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Review (2026) · cites this paper as Lin et al., 2025
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“Hidden instructions embedded in manuscripts can inflate ratings, suppress criticism, or redirect generated reviews (Ye et al., 2024; Collu et al., 2026), while character-, word-, and sentence-level perturbations can distort review judgments (Lin et al., 2025).”
From How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Review (2026) · cites this paper as Lin et al., 2025
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Research works printed but not linked: 9
- Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Sri- nath, et al. 2024. Llms assist nlp researchers: Cri- tique paper (meta-) reviewing. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.
- Linyang Li, Ruotian Ma, Qipeng Guo, Xiangyang Xue, and Xipeng Qiu. 2020. Bert-attack: Adversarial at- tack against bert using bert. In Proceedings of the Conference on Empirical Methods in Natural Lan- guage Processing.
- Fanchao Qi, Yangyi Chen, Xurui Zhang, Mukai Li, Zhiyuan Liu, and Maosong Sun. 2021a. Mind the style of text! adversarial and backdoor attacks based on text style transfer. In Proceedings of the 2021 Con- ference on Empirical Methods in Natural Language Processing.
- Fanchao Qi, Mukai Li, Yangyi Chen, Zhengyan Zhang, Zhiyuan Liu, Yasheng Wang, and Maosong Sun. 2021b. Hidden killer: Invisible textual backdoor at- tacks with syntactic trigger. In Proceedings of the An- nual Meeting of the Association for Computational.
- Keita Saito, Akifumi Wachi, Koki Wataoka, and Youhei Akimoto. 2023. Verbosity bias in preference labeling by large language models. In Proceedings of the Neural Information Processing Systems Workshop on Instruction Tuning and Instruction Following.
- Nihar B Shah, Behzad Tabibian, Krikamol Muandet, Isabelle Guyon, and Ulrike Von Luxburg. 2018. De- sign and analysis of the nips 2016 review process. Journal of machine learning research.
- Junchao Wu, Runzhe Zhan, Derek Wong, Shu Yang, Xinyi Yang, Yulin Yuan, and Lidia Chao. 2024. De- tectrl: Benchmarking llm-generated text detection in real-world scenarios. Advances in Neural Informa- tion Processing Systems.
- Jianxiang Yu, Zichen Ding, Jiaqi Tan, Kangyang Luo, Zhenmin Weng, Chenghua Gong, Long Zeng, Ren- Jing Cui, Chengcheng Han, Qiushi Sun, et al. 2024a. Automated peer reviewing in paper sea: Standard- ization, evaluation, and analysis. In Findings of the Association for Computational Linguistics: EMNLP.
- Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, and Xing Xie. 2024. Promptbench: A unified library for evaluation of large language models. Journal of machine learning research.
Cited by
Sorted by citations
The more you automate, the less you see: Hidden pitfalls of autonomous AI scientists
Luo, Z., Kasirzadeh, A., & Shah, N. B. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2610214123 · Cited by 14
When AI reviews science: Can we trust the referee?
Wang, J., Liu, Y., Xu, H., Hu, K., Di, S., Ni, W., Yue, L., Zhang, M.-L., Ren, K., & Chen, L. (2026) · The Innovation Informatics · doi:10.59717/j.xinn-inform.2026.100030 · Cited by 7
Torrielli, F., Locci, S., Rapp, A., & Di, L. (2026) · Scientometrics · doi:10.1007/s11192-026-05695-x · Cited by 6
How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Review
Li, M., Wang, C., Li, X., Zeng, X., Li, D., Shi, P., Zhou, D., & Zhou, T. (2026) · arXiv · doi:10.48550/arxiv.2608.08975
LLM-Assisted Reviewer Assignment via Auditable Expertise Matching
Bagheri, F., Buscaldi, D., & Recupero, D. R. (2026) · IEEE Access · doi:10.1109/access.2026.3724622
Metadata
- Authors
- Lin, T.-L., Chen, W.-C., Hsiao, T.-F., Liu, H.-I., Yeh, Y.-H., Chan, Y.-K., Lien, W.-S., Kuo, P.-Y., Yu, P. S., & Shuai, H.-H.
- Affiliations
- National Yang Ming Chiao Tung University (TW)
- University of Illinois Chicago (US)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2025
- Published
- 2025
- Pages
- 4819-4839
- OpenAlex
- W4416034894
- Cited by
- 6
- References
- 0
- Citations by year
- 2026: 6
- FWCI
- 5.72
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation (2346158, 2106758)
- National Science and Technology Council (NSTC-112-2221-E-A49-094-MY3)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- large language models, automated peer review, adversarial attacks, vulnerability assessment, academic writing
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- CoChat. (2026). Citation check: Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks. https://app.cochat.ai/papers/10.18653/v1/2025.findings-emnlp.259
- The paper
- Lin, T.-L., Chen, W.-C., Hsiao, T.-F., Liu, H.-I., Yeh, Y.-H., Chan, Y.-K., Lien, W.-S., Kuo, P.-Y., Yu, P. S., & Shuai, H.-H. (2025). Breaking the Reviewer: Assessing the Vulnerability of Large Language Models in Automated Peer Review Under Textual Adversarial Attacks. Findings of the Association for Computational Linguistics: EMNLP 2025. https://doi.org/10.18653/v1/2025.findings-emnlp.259
