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For better or worse, JavaScript is the cornerstone of modern Web.Prototype-based languages like JavaScript are susceptible to prototypepollution vulnerabilities, enabling an attacker to inject arbitrary propertiesinto an object's prototype. The attacker can subsequently capitalize on theinjected properties by executing otherwise benign pieces of code, so-calledgadgets, that perform security-sensitive operations. The success of an attacklargely depends on the presence of gadgets, leading to high-profile exploitssuch as privilege escalation and arbitrary code execution (ACE).  This paper proposes Dasty, the first semi-automated pipeline to helpdevelopers identify gadgets in their applications' software supply chain. Dastytargets server-side Node.js applications and relies on an enhancement ofdynamic taint analysis which we implement with the dynamic AST-levelinstrumentation. Moreover, Dasty provides support for visualization of codeflows with an IDE, thus facilitating the subsequent manual analysis forbuilding proof-of-concept exploits. To illustrate the danger of gadgets, we useDasty in a study of the most dependent-upon NPM packages to analyze thepresence of gadgets leading to ACE. Dasty identifies 1,269 server-sidepackages, of which 631 have code flows that may reach dangerous sinks. Wemanually prioritize and verify the candidate flows to build proof-of-conceptexploits for 49 NPM packages, including popular packages such as ejs,nodemailer and workerpool. To investigate how Dasty integrates with existingtools to find end-to-end exploits, we conduct an in-depth analysis of a populardata visualization dashboard to find one high-severity CVE-2023-31415 leadingto remote code execution. For the first time, our results systematicallydemonstrate the dangers of server-side gadgets and call for further research tosolve the problem.

Unveiling the Invisible: Detection and Evaluation of Prototype Pollution Gadgets with Dynamic Taint Analysis