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@envelop/graphql-modules has a Race Condition vulnerability

High severity GitHub Reviewed Published Jan 20, 2026 in graphql-hive/envelop • Updated Jan 21, 2026

Package

npm @envelop/graphql-modules (npm)

Affected versions

< 9.1.0

Patched versions

9.1.0

Description

Summary

Context race condition when using useGraphQLModules plugin

Details

Related to: GHSA-53wg-r69p-v3r7

When 2 or more parallel requests are made which trigger the same service, the context of the requests is mixed up in the service when the context is injected via @ExecutionContext() and graphql-modules are used in Yoga with useGraphQLModules(application). This issue was fixed in graphql-modules in 2.4.1 and 3.1.1 but using useGraphQLModules will bypass the async_hooks fix that was implemented.

PoC

Create the following package.json and run npm i

{
  "name": "poc",
  "scripts": {
    "compile": "tsc",
    "start": "npm run compile && node ./dist/src/index.js",
    "test": "npm run compile && node ./dist/test/bleedtest.js"
  },
  "dependencies": {
    "@envelop/graphql-modules": "^9.0.0",
    "graphql-yoga": "^5.0.0",
    "graphql": "^16.10.0",
    "graphql-modules": "3.1.1",
    "reflect-metadata": "0.2.1",
    "axios": "^1.8.4"
  },
  "devDependencies": {
    "@types/node": "^22.14.1",
    "typescript": "^5.8.3"
  }
}

Define the app entrypoint: src/index.ts

import { module } from "./module.js";
import { useGraphQLModules } from '@envelop/graphql-modules'
import { createApplication } from "graphql-modules";
import { createServer } from 'node:http'
import { randomUUID } from "node:crypto";
import { createYoga } from 'graphql-yoga';

const application = createApplication({
    modules: [module]
})

const yoga = createYoga({
    schema: application.schema,
    plugins: [useGraphQLModules(application)],
    context() {
        return {
            requestId: randomUUID(),
        }
    }
})

const server = createServer(yoga)
server.listen(4001, '127.0.0.1', undefined, () => {
    console.info(
        `[Server] Running on http://localhost:4001/graphql`
    )
})

Create the test module: src/module.ts

import { createModule, gql } from "graphql-modules";
import Service from "./service.js";

const typeDefs = gql`
    type Book {
        id1: String
        id2: String
    }
    type Query {
        books: [Book]
    }
`;

export const module = createModule({
    id: 'book-module',
    typeDefs: [typeDefs],
    providers: [Service],
    resolvers: {
        Query: {
            // return one empty book
            books: () => [{}],
        },
        Book: {
            // return the requestId from the context
            id1: async (_root, _args, { injector } ) => {
                return injector.get(Service).get();
            },
            // return the requestId from the context of the service 100 ms later
            id2: async (_root, _args, { injector } ) => {
                await new Promise(resolve => setTimeout(resolve, 100));
                return injector.get(Service).get()
            },
        }
    }
})

Add the Service that's to be injected src/service.ts

import { ExecutionContext, Injectable } from 'graphql-modules';
import 'reflect-metadata';

@Injectable()
export default class Service {
    @ExecutionContext()
    private context: ExecutionContext;

    get() {
        return this.context.requestId;
    }
}

Add the test case test/bleedtest.js

import axios from 'axios';

const url = 'http://localhost:4001/graphql';
const query = `query { books { id1 id2 } }`;

const makeGraphQLRequest = async () => {
    const response = await axios.post(url, { query });

    const book = response.data.data.books[0]
    if (book.id1 !== book.id2) {
        throw new Error(`wrong response with ids ${(book.id1)} and ${(book.id2)}`)
    }
}

const numberOfRequests = 2;
await Promise.all(Array.from(
    { length: numberOfRequests },
    makeGraphQLRequest,
));

Then run the server with npm run start in one terminal and the testcase in another with npm run test.
The returned IDs should be identical as they are both read from the context within the same request.
However, there is a mismatch:

❯ npm run test

> poc@1.0.0 test
> npm run compile && node ./dist/test/bleedtest.js


> poc@1.0.0 compile
> tsc

file://<redacted>/dist/test/bleedtest.js:8
        throw new Error(`wrong response with ids ${(book.id1)} and ${(book.id2)}`);
              ^

Error: wrong response with ids c2d83151-0922-4f25-a3e9-2f03acc1376a and e16c7335-0eaa-4386-b415-869ee4b05315
    at makeGraphQLRequest (file://<redacted>/dist/test/bleedtest.js:8:15)
    at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
    at async Promise.all (index 0)
    at async file://<redacted>/dist/test/bleedtest.js:12:1

Impact

Any application that uses useGraphQLModules from @envelop/graphql-modules along with services that inject the context using @ExecutionContext() from a singleton provider are at risk. The more traffic an application has, the higher the chance for parallel requests, the higher the risk.

References

@enisdenjo enisdenjo published to graphql-hive/envelop Jan 20, 2026
Published to the GitHub Advisory Database Jan 21, 2026
Reviewed Jan 21, 2026
Last updated Jan 21, 2026

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity High
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N

EPSS score

Weaknesses

Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')

The product contains a concurrent code sequence that requires temporary, exclusive access to a shared resource, but a timing window exists in which the shared resource can be modified by another code sequence operating concurrently. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-h3hw-29fv-2x75

Source code

Credits

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