A common assumption in low-resource NLP is that cross-lingual transfer from a related language can substitute for target-language annotation when labelled data is scarce. We test this assumption for offensive content detection across five Indic languages by evaluating all twenty directed transfer pairs from a LLaMA3.1-8B model fine-tuned with Low-Rank Adaptation (LoRA) on the MACD benchmark. Only three of twenty pairs achieve tolerable transfer loss below 15%, all involving Malayalam as the source language. Telugu is the hardest transfer target (average loss 33.8%), while Malayalam is the most transferable source (average loss 16.8%). Confusion-matrix analysis reveals two distinct failure modes: Tamiland Kannada-trained models are conservative under-flaggers that miss 73–82% of offensive content with near-zero false alarms, while Malayalam-trained models are aggressive flaggers that miss far less (39%) but over-flag at 21%. These patterns do not follow typological structure: a Spearman correlation between URIEL typological similarity and transfer F1 yields ρ = −0.254 (p = 0.281), failing to conf irm the typological hypothesis. Our results indicate that cross-lingual shortcuts are unreliable for this task and that language-specific annotation cannot be avoided by appealing to linguistic family membership.