YASA: Scalable Multi-Language Taint Analysis on the Unified AST at Ant Group

2026-01-24

AI Summary

This work proposes a multilingual static taint analysis framework based on a Unified Abstract Syntax Tree (UAST), addressing the limitations of existing tools that are typically confined to single programming languages and struggle to balance scalability and precision in heterogeneous enterprise environments. By integrating both language-agnostic and language-specific semantic modeling, the framework enables high-precision pointer analysis and cross-language taint propagation. Evaluated on standard benchmarks, it outperforms eight state-of-the-art analyzers. In real-world deployment at Ant Group, the system scanned over 100 million lines of code, uncovering 314 previously unknown taint paths and confirming 92 zero-day vulnerabilities—76 of which have already been patched—demonstrating significant improvements in both accuracy and scalability for industrial-scale, multilingual code security analysis.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• multi-language
• taint analysis
• static application security testing
• scalability
• industrial-scale

Innovation

• Unified Abstract Syntax Tree
• multi-language taint analysis
• static application security testing
• scalable static analysis
• language-agnostic semantic model