Multi-Agent Taint Specification Extraction for Vulnerability Detection

TL;DR

SemTaint combines static analysis with Large Language Models to improve taint specification extraction for JavaScript vulnerability detection, overcoming challenges posed by dynamic features and large dependency ecosystems.

Contribution

This paper introduces SemTaint, a multi-agent system that integrates LLMs with static analysis to enhance taint specification extraction for JavaScript security testing.

Findings

Detected 106 previously undetectable vulnerabilities

Identified 4 novel vulnerabilities in npm packages

Enhanced static analysis with LLMs improves vulnerability detection

Abstract

Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results compared to traditional pattern-based approaches. However, performing static taint analysis for JavaScript poses two major challenges. First, JavaScript's dynamic features complicate data flow extraction required for taint tracking. Second, npm's large library ecosystem makes it difficult to identify relevant sources/sinks and establish taint propagation across dependencies. In this paper, we present SemTaint, a multi-agent system that strategically combines the semantic understanding of Large Language Models (LLMs) with traditional static program analysis to extract taint specifications, including sources, sinks, call edges, and library flow summaries tailored to each package. Conceptually, SemTaint uses static program analysis to calculate a call graph and defers to an LLM to resolve call edges that cannot be resolved statically. Further, it uses the LLM to classify sources and sinks for a given CWE. The resulting taint specification is then provided to a SAST tool, which performs vulnerability analysis. We integrate SemTaint with CodeQL, a state-of-the-art SAST tool, and demonstrate its effectiveness by detecting 106 of 162 vulnerabilities previously undetectable by CodeQL. Furthermore, we find 4 novel vulnerabilities in 4 popular npm packages. In doing so, we demonstrate that LLMs can practically enhance existing static program analysis algorithms, combining the strengths of both symbolic reasoning and semantic understanding for improved vulnerability detection.