arXiv:2502.17832v4 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has become a common practice in multimodal large language models (MLLM) to enhance factual grounding and reduce hallucination. Yet, its reliance on retrieval exposes MLLMs to knowledge poisoning attacks, in which adversaries deliberately inject malicious multimodal content into external knowledge bases to steer models toward generating incorrect or even harmful responses. We present MM-PoisonRAG, a framework to systematically study the vulnerability of multimodal RAG under knowledge poisoning. Specifically
The rapid deployment of multimodal RAG systems makes understanding their vulnerabilities critical, as adversaries will naturally seek to exploit new attack surfaces.
This research highlights a significant security vulnerability in a foundational AI architecture, which could lead to widespread misinformation or harmful AI outputs.
The understanding of MLLM robustness is now challenged, requiring immediate focus on developing defensive mechanisms against knowledge poisoning attacks in RAG systems.
- · Cybersecurity researchers
- · AI security solution providers
- · Organizations prioritizing AI safety
- · Developers of unhardened MLLMs
- · Users relying on compromised RAG systems
- · Information integrity in public domains
Increased investment in AI security research and development for RAG systems will occur.
New industry standards or best practices for securing multimodal RAG deployments will emerge.
Adversaries may scale these attacks, potentially leading to widespread trust erosion in AI-generated information until robust defenses are broadly implemented.
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Read at arXiv cs.LG