Multimodal Large Language Models (MLLMs) have achieved impressive progress in vision-language understanding, yet they remain susceptible to object hallucination, which refers to generating descriptions of objects that do not exist in the input image. Through comprehensive analysis, we uncover a fundamental trade-off in MLLMs: strengthening visual attention enhances grounding completeness but also increases hallucinations, especially additive hallucinations that appear in the trailing part of the generated sentence. To alleviate this trade-off, we propose **GLVAC**, **G**lobal-to-**L**ocal **V**isual **A**ttention **C**orrection, a training-free dual-path inference framework that adaptively combines global conservatism and local enhancement in visual attention. The global path maintains stable and grounded predictions under moderate visual attention, while the local correction branch is dynamically activated for high-uncertainty tokens identified via entropy-based estimation. A confidence-guided gating mechanism then determines whether to adopt the correction output, ensuring safe and reliable refinement. GLVAC can be seamlessly integrated into existing MLLMs without retraining or external modules, effectively reducing object hallucinations while maintaining perceptual completeness across diverse benchmarks and model families. Our code is available on https://github.com/Trusted-LLM/GLVAC.