Large Vision-Language Models (LVLMs) are revolutionizing fields like medical diagnosis and robotics. But like their text-only cousins, they can sometimes "hallucinate," generating incorrect or nonsensical outputs. This poses significant risks, especially in safety-critical applications. So how can we tell when an LVLM is hallucinating? New research introduces "VL-Uncertainty," a clever framework that uses a model's own uncertainty to detect these hallucinations. Instead of relying on external fact-checking or labeled data, VL-Uncertainty perturbs the input prompts – slightly blurring the image and rephrasing the question in different ways, while keeping the core meaning intact. The idea is that a confident LVLM will give similar answers to slightly different versions of the same question. If the answers vary wildly, it suggests the model is uncertain and might be hallucinating. This approach, tested across various LVLMs and benchmarks, has shown promising results in flagging hallucinations without needing external resources. This opens doors for more reliable and safer use of LVLMs in real-world applications where accuracy is paramount. Future research could explore incorporating these uncertainty estimations directly into the model's training process to proactively reduce hallucinations, leading to even more robust and trustworthy AI systems.