With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over contradicting information to derive correct answers. This urgency gives rise to the need to make QA systems robust to misinformation, a topic previously unexplored. We study the risk of misinformation to QA models by investigating the behavior of the QA model under contradicting contexts that are mixed with both real and fake information. QA, which contains over 10K human-written and model-generated contradicting pairs of contexts. Experiments show that QA models are vulnerable under contradicting contexts brought by misinformation. To defend against such threat, we build a misinformation-aware QA system as a counter-measure that integrates question answering and misinformation detection in a joint fashion. A typical Question Answering (QA) system (Chen et al., 2017; Yang et al., 2019; Karpukhin et al., 2020; Lewis et al., 2020b) starts by retrieving a set of relevant context documents from the Web, which are then examined by a machine reader to identify the correct answer. Existing work equate Wikipedia as the web corpus. Therefore, all retrieved context documents are assumed to be clean and trustable. However, real-world QA faces a much noisier environment, where the web corpus is tainted with misinformation.