This comprehensive survey analyzes why multimodal large language models (MLLMs) generate outputs inconsistent with visual content—a phenomenon known as hallucination.

• Reliability gaps: MLLMs often produce plausible but factually incorrect interpretations of images
• Security implications: Unreliable AI outputs raise concerns for critical applications and real-world deployments
• Practical obstacles: Hallucinations significantly limit the trustworthiness of these advanced systems
• Technical assessment: The survey evaluates current benchmarks and mitigation strategies

For security professionals, this research highlights substantial vulnerabilities in multimodal AI systems that must be addressed before deployment in sensitive environments.

Hallucination of Multimodal Large Language Models: A Survey