mobile OSes, the channel access functions of Wi-Fi protocol, i.e., IEEE 802.11, can be manipulated \"selfishly\", at the cost of other nearby well-behaving Wi-Fi device performance. Note that the newest IEEE 802.11 ax standard (aka Wi-Fi 6) [5] has been investigating adaptive carrier sensing (CS), which is also known as dynamic sensitivity control (DSC), in order to improve the spatial reuse in dense deployments [6]. This implies that the parameters of Wi-Fi tethering, particularly Clear Channel Assessment (CCA) thresholds are readily tunable and can be manipulated in order to gain an unfair advantage in throughput performance. Thus, it is essential to design an efficient mechanism in order to detect unauthorized and misconfigured Wi-Fi tethering. In this paper, we observe the network throughput behavior in a multi-AP environment in the presence of a tethering device, and study the impact of selfish tethering on other users' throughput performance. We show that the abuse of Wi-Fi tethering that adjusts the CCA threshold has strong adverse effects in Wi-Fi networks, while providing the tethered device a high throughput gain. Our evaluation results reveal that the symptoms of selfish tethering resembles that of the well-known hidden node problem--they both result in a high frame transmission error rate. However, the main difference is that the frame losses at legitimate nodes that are caused by the selfish tethering cannot be easily resolved by the RTS/CTS mechanism, because the selfish nodes may not recognize or sense the RTS/CTS frames due to their manipulated CCA thresholds. Therefore, it is imperative to design a detection mechanism that can identify the root cause of the throughput degradation and pinpoint the selfish node in the network. To this end, we present an online detection mechanism, called CUBIA, which can accurately detect misbehaving Wi-Fi tethering nodes in a multi-AP Wi-Fi network. CUBIA runs at each AP and passively monitors the ongoing data traffic to detect any abnormal changes caused by target selfish tethering. In particular, it monitors any noticeable increase in frame loss rate and identifies the root cause of the frame loss, e.g., selfish tethering or hidden node problem. For this, we formulate the selfish tethering detection problem as a hypothesis testing problem and employs a modified CUSUM algorithm. We evaluate CUBIA with an in-depth simulation in various wireless environments, and our evaluation results show high detection accuracy. To best of our knowledge, this is the first paper to consider the problem of detecting selfishly misconfigured Wi-Fi tethering devices in a multi-AP network. **Contributions. ** This paper makes several contributions, as follows. * We observe that concurrent transmission mechanisms, e.g., PHY capture or message-in-message (MIM), can be abused by selfish nodes, and study the impact of the selfish Wi-Fi tethering behavior on the throughput performance of other nearby well-behaving nodes. * We propose a selfish misbehavior detection mechanism, called CUBIA, which can accurately identify the cause of frame losses and detect selfish behavior under strict detection latency requirements. * We design a two-step detection process using an online change detection algorithm, i.e., CUSUM, which is based on passive monitoring of frame loss rates at multiple APs. We also study the impact of detection thresholds on detection latency/accuracy performance. * We perform extensive simulation-based evaluation for various network conditions. Our evaluation results show that CUBIA can promptly detect selfish behavior with high accuracy in realistic wireless environments. **Organization. ** The remainder of paper is organized as follows. Section 2 describes the system model and illustrates the impact of selfish manipulation of the CCA threshold (CST) on the throughput performance in multi-AP environments. Section 3 identifies the unique features of selfish tethering and proposes CUBIA that accurately detects the selfish behavior. Section 4 presents the evaluation results.