With the widespread deployment and application of Multimodal Large Language Models (MLLMs), disputes over model ownership have become increasingly frequent, raising serious concerns about intellectual property protection in black-box scenarios. Existing techniques primarily embed copyright information into models through backdoor or adversarial attacks, triggering abnormal outputs on specific inputs for verification. However, we find that such methods exhibit limited robustness after post-processing operations such as fine-tuning, pruning, and model merging. To address this, we propose Concept-based Probabilistic Fingerprinting (CPF). CPF selects a visual concept as the fingerprint trigger, embeds statistical watermark signals in concept-related multimodal conversations, and migrates the watermarked distribution to the original model through self-distillation. It further employs cross-concept similarity screening to construct a regularization constraint, ensuring the fingerprint is activated only on the target concept. CPF does not trigger semantically irrelevant abnormal outputs, nor does it rely on fixed inputs as triggers. Experimental results demonstrate that CPF maintains stable fingerprint detectability across diverse scenarios while exhibiting strong robustness.