Safety unlearning for multimodal large language models (MLLMs) fails when it treats danger as an object, topic, or prompt template. A useful MLLM must still recognize risky visual evidence and answer benign educational or preventive questions about the same content. The safety-critical target is narrower: the post-fusion interaction that converts risky visual evidence and unsafe textual intent into actionable harmful assistance. Existing refusal-oriented and unlearning objectives reach this mechanism only indirectly, widening refusal boundaries or leaving recoverable multimodal harmful pathways. We propose CMIE, Cross-Modal Interaction Erasure, a mechanism-level method that directly treats the harmful image-text interaction as the object of erasure. CMIE first estimates harmful interaction directions with intent-matched counterfactual contrasts that cancel unimodal main effects. It then localizes behaviorally active post-fusion layers and estimates a safe-whitened low-rank harmful subspace. A closed-form minimum-distortion weight edit erases this subspace, and projection-constrained recovery restores benign utility without rewriting erased directions. Across SafeEraser, VLGuard, and MSSBench, CMIE achieves the best overall mean rank and outperforms strong baselines on multiple safety, utility, visual selectivity, and paired-context metrics. Warning: This paper contains examples of harmful language and images, and reader discretion is recommended.