This paper presents BSan, a practical software-only memory error detector for binary code. Different from state-of-the-art binary-level detectors, which rely on either the shadow memory-based approach or the hardware-specific feature and thus suffer from several fundamental limitations, BSan adopts an identifier-based approach, enabling it to detect deep memory errors missed by existing detectors. Also, BSan does not depend on any specific hardware features. To reduce the high performance overhead caused by identifier propagation, BSan creates a novel hybrid approach, static analysis+dynamic instrumentation, to improve the performance without inheriting the poor reliability of static binary rewriting, distinguishing it from existing detectors that simply refer to static binary rewriting for better performance. The comprehensive evaluation demonstrates that BSan can detect more memory errors than state-of-the-art binary-level detectors. Meanwhile, the performance and memory overheads of BSan are comparable to those of existing detectors.