Drawing on recent work concerning the statistical robustness of inequality statistics we examine the sensitivity of poverty indices to data contamination using the concept of the influence function. We show that poverty and inequality indices have fundamentally different robustness properties, and demonstrate that an important commonly used subclass of poverty measures will be robust under data contamination. We investigate both the case where the poverty line is exogenously fixed and where it must be estimated from the data.