Abstract: Global navigation satellite system (GNSS) has become a critical infrastructure for mass-market intelligent terminals, such as smartphones, vehicle-mounted terminals, consumer unmanned aerial vehicles, and wearable devices, to obtain position, velocity, and time information. Due to the extremely low received power of civil GNSS signals at ground level and their fully open signal structure, and because massmarket terminals generally lack security capabilities such as antenna arrays, dedicated radio-frequency monitoring, and cryptographic authentication, they are highly vulnerable to both jamming and spoofing threats in complex electromagnetic environments and intentional attack scenarios. Specifically, jamming mainly degrades receiver acquisition, tracking, and positioning performance by raising the noise floor or injecting radio-frequency signals with specific spectral characteristics, thereby disrupting navigation service outright; spoofing, by contrast, induces terminals to output incorrect but seemingly plausible position, velocity, or time results by forging, replaying, or generating high-fidelity GNSS-like signals, which poses far more insidious and destructive risks. Focusing on the specific application scenario of mass-market intelligent terminals, we first clarify the classification system of GNSS intentional interference threats and the set of terminal-observable measurements, then systematically review the research progress of jamming and spoofing detection techniques. Emphasis is placed on the applicability, deployment cost, and inherent limitations of seven categories of methods based on signal strength and receiver status, spectrum and statistical features, raw measurement consistency, crowdsourced collaborative sensing, motion constraints, multi-sensor fusion, and machine learning. In addition, we summarize the publicly available datasets, experimental platforms, and performance evaluation metrics in this field, and systematically discuss future directions in terms of real attack data acquisition, robustness in complex urban environments, cross-device generalization, low-power real-time detection, trustworthy multi-source fusion, edge-cloud collaboration, and explainable artificial intelligence. The review concludes that GNSS intentional interference detection for massmarket intelligent terminals should not simply copy anti-interference schemes designed for professional receivers. Instead, it should fully exploit terminal-native observations, multi-source auxiliary information, and collaborative sensing capabilities to develop a layered, lightweight, and verifiable detection framework.