Does AI Reviewer See the Full Picture? Attacking and Defending Multimodal Peer Review
Quick Answer
This paper shows that The integration of Large Language Models (LLMs) into peer review exposes vulnerabilities to targeted attacks, prompting the introduction of PaperGuard, a benchmark designed to evaluate and defend against these multimodal adversarial manipulations.
Quick Take
The framework includes a multimodal dataset, a suite of targeted attacks, and a defense mechanism using chunk-based embedding search, revealing that AI reviewers are significantly susceptible to manipulation.
Key Points
- Current AI peer-review studies focus primarily on text, neglecting multimodal vulnerabilities.
- PaperGuard features a comprehensive dataset across various scientific domains.
- The framework includes black-box and white-box attack methodologies targeting both text and figures.
- Experiments confirm that AI reviewers are widely vulnerable to domain-specific attacks.
- PaperGuard establishes essential protocols for resilient AI-assisted scholarly reviewing.
Paper Resources
Source Excerpt
arXiv:2606. 12716v1 Announce Type: new Abstract: The integration of (LLMs) and Multimodal LLMs (MLLMs) into scientific peer-review workflows introduces novel and significant risks for adversarial manipulation, especially given the multimodal nature of scientific papers where figures, not just text, convey core evidence. This creates a significant gap: current robustness studies on AI peer-review are overwhelmingly text-only. …
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