In this paper we describe and evaluate a technique to improve the amount of information gained from dynamic malware analysis systems. By playing network games during analysis, we explore the behavior of malware when it believes its network resources are malfunctioning. This forces the malware to reveal its alternative plan to the analysis system resulting in a more complete understanding of malware behavior. Network games are similar to multipath exploration techniques, but are resistant to conditional code obfuscation. Our experimental results show that network games discover highly useful network information from malware. Of the 161,000 domain names and over three million IP addresses coerced from malware during three weeks, over 95% never appeared on public blacklists. We show that this information is both likely to be malicious and can be used to improve existing domain name and IP address reputation systems, blacklists, and network-based malware clustering systems.