For accommodating rapidly increasing power demands, power systems are transitioning from analog systems to systems with increasing digital control and communications. Although this modernization brings many far-reaching benefits, the hardware and software newly incorporated into the power systems also incur many vulnerabilities. By taking advantage of these vulnerabilities, adversaries can launch various cyber/physical attacks to tamper with electricity meter readings, i.e., to steal electricity. It is reported that total worldwide annual economic losses caused by electricity theft reached up to almost one hundred billion dollars in recent years. With methods to tamper with meter readings becoming more versatile, secret, and flexible, electricity theft tends to get even more serious in modernized power systems. For preventing adversaries from stealing electricity, researchers have done a lot of works. Although some related surveys on these works exist, they are not updated or just discuss electricity theft in a specific region. This survey aims to gain a comprehensive and in-depth understanding of the electricity theft issue. After investigating how adversaries tamper with meter readings, we systematically survey all existing detection methods up to date, which is classified into machine learning- and measurement mismatch-based methods. Adverse effects and political and socioeconomic factors of electricity theft are also provided. This survey can help relevant researchers to shape future research directions, especially in the area of developing new effective electricity theft detection methods.