Vision-language models (VLMs) are increasingly used through black-box, open-ended interfaces, where input-level backdoor screening must work without model parameters, gradients, logits, or training data. We present GPC-E4, a detector for single image-prompt queries that probes a VLM with mild, semantics-preserving visual and textual perturbations. The method measures whether the output behavior contracts unusually under these probes: GPC captures dominant-mode or semantic consistency, while E4 summarizes predictive entropy, augmentation mutual information, minimal collapse, and local slope along perturbation paths. A fixed robust normalization combines these signals and can operate without a known-clean calibration set. Across COCO, Flickr30k, Caltech101, and MMBench-style evaluations with LLaVA-1.5, MiniGPT-4, CLIP, CoOp, and Qwen3-VL, GPC-E4 outperforms STRIP, SCALE, TeCo, and BDet in both closed-set and open-ended settings. Additional analyses under alternative stressors, prompt and distribution shifts, zero clean calibration, VLM-native attacks, and detector-aware attacks characterize when the contraction signal is reliable and when it weakens.