Differential morphing attack detection (D-MAD) compares a trusted live capture with a document image that may contain a facial morph. We investigate whether labelled validation data can improve a frozen-parameter vision-language evaluator through prompt selection rather than model fine-tuning.

Version1 generates 50 candidate prompts and selects the top-ranked instruction using a security-oriented loss that prioritises missed morph attacks (MACER) while accounting for bona fide classification errors (BSCER), response failures, parse failures, and decision inconsistency. Version2 recursively revises prompts using aggregate validation feedback. On a subject-disjoint test set of 160 pairs spanning LMA, MIPGAN-2, and PIPE, the selected Version1 prompt achieves a (19.17%) morph attack classification error rate (MACER), with (0.00%) observed bona fide sample classification error rate (BSCER), compared with (70.00%) MACER for a controlled manual prompt. The paired (50.83) percentage-point difference has a (95%) subject-bootstrap interval of ([43.33,58.33]).

Recursive revision does not surpass the best Generation0 validation loss; the reported revised descendant obtains (31.67%) test MACER. All 23 attacks missed by Version~1 are landmark-based (LMA) morphs.

Overall, the results show that prompt formulation can substantially influence frozen-parameter VLM-based D-MAD performance, while also highlighting the challenge of recursive prompt refinement under a compact 10-subject validation setting.