Web applications encompass various aspects of daily life, including online shopping, e-learning, and internet banking. Once there is a vulnerability, it can cause severe societal and economic damage. Due to its ease of use, PHP has become the preferred server-side programming language for web applications, making PHP applications a primary target for attackers. Data flow analysis is widely used for vulnerability detection before deploying web applications because of its efficiency. However, the high complexity of the PHP language makes it difficult to achieve precise data flow analysis. In this paper, we present Yama, a context-sensitive and path-sensitive interprocedural data flow analysis method for PHP, designed to detect taint-style vulnerabilities in PHP applications. We have found that the precise semantics and clear control flow of PHP opcodes enable data flow analysis to be more precise and efficient. Leveraging this observation, we established parsing rules for PHP opcodes and implemented a precise understanding of PHP program semantics in Yama. We evaluated Yama from three dimensions: basic data flow analysis capabilities, complex semantic analysis capabilities, and the ability to discover vulnerabilities in real-world applications, demonstrating Yama's advancement in vulnerability detection. Specifically, Yama possesses context-sensitive and path-sensitive interprocedural analysis capabilities, achieving a 99.1% true positive rate in complex semantic analysis experiments related to type inference, dynamic features, and built-in functions. It discovered and reported 38 zero-day vulnerabilities across 24 projects on GitHub with over 1,000 stars each, assigning 34 new CVE IDs. We have released the source code of the prototype implementation and the parsing rules for PHP opcodes to facilitate future research.