In the era of ubiquitous short-video sharing, an overlooked yet significant privacy risk has arisen: the accidental disclosure of confidential information through reflective surfaces, such as mirrors, glass, or even polished metal. Such reflections can inadvertently disclose sensitive personal details to a broad audience without the awareness of content creators. Our examination of 100 top-rated TikTok short videos reveals that, on average, 37.2% of frames in each video feature identifiable reflective surfaces, posing potential privacy risks. In this work, we introduce a framework designed to scrutinize reflective privacy risks in glass-laden short videos. At the heart of the framework is the development of a reflection-specific neural radiance field, termed RP-NeRF, which enables reflection-aware ray tracing for precise extraction and reconstruction of the reflective scenes from the surfaces they appear on. A detailed field study on the framework indicates that the precision in detecting human presence and recognizing objects from the reconstructed reflective images reaches as high as 90.8% and 89.6%, respectively, even when dealing with a reflective surface that boasts 90% transparency and a mere 4% reflectance rate. These findings highlight the urgent need for greater awareness and advanced solutions to safeguard privacy in our digital age, especially in light of the significant impact of short-video sharing.