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

arXiv: https://arxiv.org/abs/2601.17390 · HTML: https://mlsanity.com/paper/2601.17390/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2026.

## Built on by (most cited first, 4 of 4)

- [MalSkills: Detecting Malicious Skills in the Agentic Supply Chain via Neuro-symbolic Reasoning](https://mlsanity.com/paper/2603.27204/) (2026)
- [AgentFlow: Building Agent Dependency Graphs for Static Analysis of Agent Programs](https://mlsanity.com/paper/2607.01640/) (2026)
- [Demystifying and Detecting Agentic Workflow Injection Vulnerabilities in GitHub Actions](https://mlsanity.com/paper/2605.07135/) (2026)
- [Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents](https://mlsanity.com/paper/2610.03014/) (2026)

## Abstract (arXiv)

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.