arXiv:2606.13044v1 Announce Type: new 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
As AI-generated reviews transition into academic and professional peer-review infrastructures, understanding their vulnerabilities becomes critical to maintaining scientific integrity.
This research reveals a subtle yet potent attack vector against AI peer review that does not rely on explicit prompt manipulation, highlighting the sophistication required for robust AI governance.
The understanding of AI peer review robustness shifts from focusing primarily on prompt injection to recognizing the vulnerability to 'presentation-only' adversarial attacks, necessitating new defense strategies.
- · Researchers developing AI robustness defenses
- · Organizations focused on AI ethics and responsible AI deployment
- · AI systems without advanced adversarial robustness training
- · Academic and publication bodies adopting AI peer review without sufficient safeg
AI review systems will need to be developed with a deeper understanding of human cognitive biases and narrative manipulation.
The findings could lead to a 'red team' approach where AI systems are designed to identify and exploit such subtle presentation-level attacks before deployment.
This could potentially foster a new field of 'adversarial presentation design' where authors learn to optimally frame their work for both human and AI reviewers.
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Read at arXiv cs.CL