Big data processing frameworks such as Hadoop [1] have been widely adopted in the past few years. However, the security issues in such large scale systems have not been well studied yet. While most of the prior work is focused on the data privacy and protection, this paper investigates a potential attack from a compromised internal node against the overall system performance. We explore the vulnerabilities of the existing Hadoop system, and develop an effective attack launched from the compromised node that can significantly degrade the data processing performance of the cluster without being detected and blacklisted for job execution. In addition, we present a mitigation scheme that protects a Hadoop system from such attack. We conduct experiments on real systems, and the results show that this attack greatly slows down the job executions in the native Hadoop system even with some basic defense mechanisms. Our mitigation scheme, while causing a minor overhead in normal circumstances, can keep the whole cluster running efficiently under this attack from the compromised internal node.