Multimodal large language models (MLLMs) trained on image-text data may retain sensitive information in textual memories, visual representations, and cross-modal associations, creating a need for reliable unlearning. Although existing unlearning methods can reduce target disclosure under clean queries, suppressed knowledge may remain recoverable under adversarial conditions. We introduce a new perspective on this vulnerability by assessing post-unlearning recovery through conditional perturbation generation, where a learned generator maps each image-text input to perturbations that promote recovery of the suppressed response. This framework is instantiated with two generators: CEPG injects conditional soft tokens into the multimodal input embedding sequence, while CPPG generates pixel-space perturbations under CLIP-based semantic conditions. Experiments across five unlearning methods show strong recovery risks. CEPG achieves average recovery ratio (Rec) scores of 60.19% on MLLMU-Bench and 46.74% on CLEAR, while CPPG attains 34.28% and 29.47%, respectively. Both attacks remain effective with limited or no direct access to forgotten samples, highlighting that current unlearning offers insufficient robustness guarantees. To mitigate this vulnerability, we introduce Adversarially Robust Unlearning (ARU), which integrates conditional recovery attacks into unlearning with attribution-guided parameter selection, significantly reducing recovery rates while preserving utility on retain sets.