Modern enterprises increasingly adopt diverse technology stacks with various programming languages, posing significant challenges for static application security testing (SAST). Existing taint analysis tools are predominantly designed for single languages, requiring substantial engineering effort that scales with language diversity. While multi-language tools like CodeQL, Joern, and WALA attempt to address these challenges, they face limitations in intermediate representation design, analysis precision, and extensibility, which make them difficult to scale effectively for large-scale industrial applications at Ant Group. To bridge this gap, we present YASA (Yet Another Static Analyzer), a unified multi-language static taint analysis framework designed for industrial-scale deployment. Specifically, YASA introduces the Unified Abstract Syntax Tree (UAST) that provides a unified abstraction for compatibility across diverse programming languages. Building on the UAST, YASA performs point-to analysis and taint propagation, leveraging a unified semantic model to manage language-agnostic constructs, while incorporating language-specific semantic models to handle other unique language features. When compared to 6 single- and 2 multi-language static analyzers on an industry-standard benchmark, YASA consistently outperformed all baselines across Java, JavaScript, Python, and Go. In real-world deployment within Ant Group, YASA analyzed over 100 million lines of code across 7.3K internal applications. It identified 314 previously unknown taint paths, with 92 of them confirmed as 0-day vulnerabilities. All vulnerabilities were responsibly reported, with 76 already patched by internal development teams, demonstrating YASA's practical effectiveness for securing large-scale industrial software systems.

翻译：现代企业日益采用多编程语言构建的不同技术栈，这给静态应用安全测试带来了重大挑战。现有污点分析工具主要针对单一语言设计，需要随语言多样性扩展的大量工程投入。尽管CodeQL、Joern和WALA等多语言工具试图解决这一问题，但在中间表示设计、分析精度和可扩展性方面存在局限，难以有效支撑蚂蚁集团的大规模工业级应用。为弥补这一差距，我们提出YASA（Yet Another Static Analyzer），一个面向工业级部署的统一多语言静态污点分析框架。具体而言，YASA引入统一抽象语法树，通过统一的抽象层实现对多种编程语言的兼容性。基于UAST，YASA执行指向分析和污点传播，利用统一语义模型管理语言无关的结构，同时结合语言特定语义模型处理其他独特语言特性。在行业标准基准测试中与6个单语言和2个多语言静态分析器对比，YASA在Java、JavaScript、Python和Go上均持续优于所有基线方法。在蚂蚁集团的实际部署中，YASA分析了7.3K个内部应用中的超过1亿行代码，识别出314条先前未知的污点路径，其中92条被确认为零日漏洞。所有漏洞均得到负责任报告，其中76个已被内部开发团队修补，验证了YASA在保护大规模工业软件系统方面的实际有效性。