Hunting Down The React2Shell Vulnerability Across Enterprise Codebases (Part 1)
find-affected-code-react-server-components-critical-security-vulnerability-cve-2025-55182
What is CVE-2025-55182?
A critical security vulnerability (CVE-2025-55182, CVSS 10.0) in React Server Components was reported by Lachlan Davidson on November 29th, 2025.
This severe flaw allows for unauthenticated remote code execution. It exploits how React decodes payloads sent to React Server Function endpoints.
Affected Versions and Scope:
- Any application supporting React Server Components is potentially vulnerable, even if it does not explicitly use React Server Function endpoints.
- The vulnerability affects the following packages in versions 19.0, 19.1.0, 19.1.1, and 19.2.0: react-server-dom-webpack react-server-dom-parcelreact-server-dom-turbopack
Find everywhere React Server Components are used across all your code
To ensure comprehensive coverage, it is critical to check not only for direct usage of the vulnerable packages, but also for dependent frameworks and libraries that incorporate them.
Run these queries on Sourcegraph to quickly identify which projects directly or indirectly depend on vulnerable versions of React Server Components. The following links display results on Sourcegraph’s public code search across 1.1 million open source repositories.
Direct dependencies that have vulnerable versions of React Server Components:
- Next.js
- React Router
- react-server-dom-(webpack|parcel|turbopack)
Note: Searching strictly for exact version numbers can miss dependencies that appear to be upgradable but are pinned to vulnerable versions in yarn.lock or package-lock.json. We target ^ and ~ prefixes to catch repositories that haven't explicitly closed the door on the vulnerability.
Search across your organization's private code
Code Search
You can utilize either our CLI or our web app. We've linked to our public code search above; feel free to modify those URLs or use the following syntax:
react-server-dom (webpack, parcel, and turbopack)
context:global file:package.json "react-server-dom-(webpack|parcel|turbopack)":\s*"[~^]?(19\.0(\.0)?|19\.1\.[01]|19\.2\.0)" patterntype:regexp*
Next.js
context:global file:package.json "next":\s*"[~^]?(15\.0\.[0-4]|15\.1\.[0-8]|15\.2\.[0-5]|15\.3\.[0-5]|15\.4\.[0-7]|15\.5\.[0-6]|16\.0\.[0-6])" patterntype:regexp*
React Router
context:global file:package.json ("react-router" OR "@remix-run/router") AND "react-server-dom-(webpack|parcel|turbopack)":\s*"[~^]?(19\.0(\.0)?|19\.1\.[01]|19\.2\.0)" patterntype:regexp*
Using the Sourcegraph CLI to search for the CVE-2025-55182 vulnerability.
Deep Search
Deep Search is an agentic code search tool designed to understand and execute complex natural language queries. It conducts exhaustive searches to deliver comprehensive answers and facilitates more in-depth investigations through follow-up questions. For example, you can use natural language to search for vulnerabilities, such as CVE-2025-55182.
The vulnerability, identified as CVE-2025-55182, affects any application that supports React Server Components. The affected package versions are 19.0, 19.1.0, 19.1.1, and 19.2.0. Please check all github.com/sourcegraph/* repositories for use of these vulnerable versions.*
Executing the prompt with Deep Search
Stay tuned for Part 2, which covers fixing and tracking your vulnerable code.
Getting started with Sourcegraph
Schedule a conversation to see how Sourcegraph can help you and your team find code, make large-scale changes, and track insights across codebases of any scale and with any number of code hosts.
Special thanks to Tino Wening, Stephanie Jarmak, and Dan Adler for their valuable feedback on this post.
Sourcegraph Blog
https://webflow.sourcegraph.com/blog/find-affected-code-react-server-components-critical-security-vulnerability-cve-2025-55182Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
componentAn Information-Theoretic Method for Dynamic System Identification With Output-Only Damping Estimation
arXiv:2603.29956v1 Announce Type: cross Abstract: The system identification capabilities of a novel information-theoretic method are examined here. Specifically, this work uses information-theoretic metrics and vibration-based measurements to enhance damping estimation accuracy in mechanical systems. The method refers to a key limitation in system identification, signal processing, monitoring, and alert systems. These systems integrate various components, including sensors, data acquisition devices, and alert mechanisms. They are designed to operate in an environment to calculate key parameters such as peak accelerations and duration of high acceleration values. The current operational modal identification methods, though, suffer from limitations related to obtaining poor damping estimates
Counterfactual Analysis of Brain Network Dynamics
arXiv:2603.29843v1 Announce Type: new Abstract: Causal inference in brain networks has traditionally relied on regression-based models such as Granger causality, structural equation modeling, and dynamic causal modeling. While effective for identifying directed associations, these methods remain descriptive and acyclic, leaving open the fundamental question of intervention: what would the causal organization become if a pathway were disrupted or externally modulated? We introduce a unified framework for counterfactual causal analysis that models both pathological disruptions and therapeutic interventions as an energy-perturbation problem on network flows. Grounded in Hodge theory, directed communication is decomposed into dissipative and persistent (harmonic) components, enabling systemati
Wherefore Art Thou? Provenance-Guided Automatic Online Debugging with Lumos
arXiv:2603.29013v1 Announce Type: new Abstract: Debugging distributed systems in-production is inevitable and hard. Myriad interactions between concurrent components in modern, complex and large-scale systems cause non-deterministic bugs that offline testing and verification fail to capture. When bugs surface at runtime, their root causes may be far removed from their symptoms. To identify a root cause, developers often need evidence scattered across multiple components and traces. Unfortunately, existing tools fail to quickly and automatically record useful provenance information at low overheads, leaving developers to manually perform the onerous evidence collection task. Lumos is an online debugging framework that exposes application-level bug provenances--the computational history link
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Generative UI
US calls Taiwan 'vital partner' after high-level tech and AI talks - Reuters
<a href="https://news.google.com/rss/articles/CBMirAFBVV95cUxOMUZaakpFSkk5NnI3dlVZWEJsTzJpbjl4LWswV0daOUNXdmFidUZrZVFSQUNVZFhmcDRXWTU3QmdwVHpaSU81YU1oYlBBQUw0MWIzTGQtdHROaEhpaUZrVEQ3TUc2LTJDbjlNSlM4b0hJZTY5bFJYaGt0UzVxS21jTXlKMVBTWFN0N2d1R0tkUjlKbzFwOG9SOFB0dmNmdWl5SVl6SGRrNWJ0RkNK?oc=5" target="_blank">US calls Taiwan 'vital partner' after high-level tech and AI talks</a> <font color="#6f6f6f">Reuters</font>
Perplexity AI accused of sharing users’ personal data with Meta, Google - The Straits Times
<a href="https://news.google.com/rss/articles/CBMiqwFBVV95cUxNWkk4ZHJKRGNSR3FKdHNjTzJnU3Q5NVlSakxxc3NjMEstYU9fNWdZMnJIcy01R1NPdlJfVDNPeGlSN2VvMnJad2labVc3cFVGVWZPbjk1SkZwQS1QcEFVSnFLa3BIc0dHWEJRcDhTeV9VSFlMMFI5d1FjN0lYQkk5QU9YNEZnSlBpS01ZenBGTXVESHROV0doOTQzQ1ktdGlCVEZncHFJWmtKaEE?oc=5" target="_blank">Perplexity AI accused of sharing users’ personal data with Meta, Google</a> <font color="#6f6f6f">The Straits Times</font>
NVIDIA and Marvell form partnership to expand AI ecosystem - New Electronics
<a href="https://news.google.com/rss/articles/CBMipwFBVV95cUxNbmRDRDZTYWdjMnlZSzZKQS1SZmZ4Y0Z1azlzTGFGU3pFa1FFcjU3OTNveC1fUUdIbkwwZkkxUE5MRXcwQjVrYkRwU2NaWHFqRDA2OHRMamRmMXdNd0xUZjFTN1dZekhSSWp3XzFQNUxpWG5Seld0ZExoY2NPTWVVQWQzbHRFcm40YXdnYnpMMmZhM2VReWxpNmVEbEZTV081dVI1cjBIOA?oc=5" target="_blank">NVIDIA and Marvell form partnership to expand AI ecosystem</a> <font color="#6f6f6f">New Electronics</font>
NVIDIA Invests $2B into Marvell | Business | Mar 2026 - Photonics Spectra
<a href="https://news.google.com/rss/articles/CBMifEFVX3lxTE8wZDFUVUxDQUJHOUVjM1NTN1VreFJ6SGRlaVRDVW9FR1JIVkV0SUN3c1ZzTWZJQ1RDcnF4czFzX3NWaUgxRDZ4Z2VINGZLM1h2YVhoMlBCS181VkpHSjVzbkdQNjdDWGsxUmRuZEtvcmNUbmVFaW0yOEFoSTM?oc=5" target="_blank">NVIDIA Invests $2B into Marvell | Business | Mar 2026</a> <font color="#6f6f6f">Photonics Spectra</font>
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!