We study influence diagnostics for generalized linear models when the true covariates are unobservable but measured with error. Based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. The magnitude of influence is then assessed via a simulated envelope approach. The proposed diagnostic procedure is illustrated on two examples.