arXiv:2606.12716v1 Announce Type: new Abstract: The integration of Large Language Models (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. Moreover, the problem is distinct from standard jailbreaking, as a peer-review attack seeks to induce a domain-specific, targeted failure (e.g., "infl
The rapid integration of LLMs and MLLMs into professional workflows, including critical functions like peer review, makes studying their vulnerabilities particularly timely.
This highlights emerging attack surfaces in AI-driven professional systems, pushing for more robust and secure AI integration, especially in high-stakes domains like scientific validation.
The focus extends from general AI safety to specific adversarial attacks on domain-specific AI applications, particularly those handling multimodal inputs.
- · AI robustness researchers
- · Cybersecurity firms specializing in AI
- · Open science advocates who benefit from robust peer review
- · AI systems lacking multimodal robustness
- · Organizations deploying unhardened AI review systems
- · Scientific publications vulnerable to manipulation
Increased research and development efforts will focus on building more resilient AI models capable of identifying and resisting adversarial attacks in complex data environments.
New standards and best practices will emerge for the secure deployment of AI in critical review processes, potentially leading to 'AI safety certifications' for review tools.
The arms race between AI attackers and defenders could lead to more sophisticated AI-driven deception and detection mechanisms, impacting the integrity of information in various domains beyond scientific review.
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Read at arXiv cs.CL