Linear probes can recover deception-related signals from large language model activations, but existing evidence is overwhelmingly English-centric. We investigate whether this discrimination transfers across languages and what representational geometry accompanies such transfer. We train instructed-deception probes on controlled honest/deceptive instruction contrasts in English, Portuguese, Spanish, German, and Chinese, and evaluate them without target-language refitting on a multilingual adaptation of DeceptionBench across five models spanning four different families. Cross-language performance remains close to matched-language baselines, with all models satisfying our prespecified noninferiority criterion, although absolute discrimination varies substantially across models. We also find that language-specific probe directions are strongly aligned and concentrate around a shared consensus direction, while response representations retain clear language-specific organization. Additional construct-validity analyses show that measurable response-level cues, including style, sentiment, factual truthfulness, and length, explain part but not all of the strongest probe discrimination. These findings together suggest that cross-lingual transfer and shared probe geometry can coexist with model-dependent detection quality and persistent language structure, indicating that representational alignment alone is insufficient evidence of reliable multilingual deception monitoring.