Find, fix and prevent vulnerabilities in your code.

Overview

node-sass is a Node.js bindings package for libsass.

Affected versions of this package are vulnerable to NULL Pointer Dereference in the function Sass::Functions::selector_append which could be leveraged by an attacker to cause a denial of service (application crash) or possibly have unspecified other impact. node-sass is affected by this vulnerability due to its bundled usage of libsass.

Remediation

There is no fixed version for node-sass.

References

• GitHub Commit
• GitHub Commit
• POC: GitHub Issue

Overview

node-sass is a Node.js bindings package for libsass.

Affected versions of this package are vulnerable to Use After Free via the SharedPtr class in SharedPtr.cpp (or SharedPtr.hpp) that may cause a denial of service (application crash) or possibly have unspecified other impact. Note: node-sass is affected by this vulnerability due to its bundled usage of the libsass package.

Details

Cross-site scripting (or XSS) is a code vulnerability that occurs when an attacker “injects” a malicious script into an otherwise trusted website. The injected script gets downloaded and executed by the end user’s browser when the user interacts with the compromised website.

This is done by escaping the context of the web application; the web application then delivers that data to its users along with other trusted dynamic content, without validating it. The browser unknowingly executes malicious script on the client side (through client-side languages; usually JavaScript or HTML) in order to perform actions that are otherwise typically blocked by the browser’s Same Origin Policy.

Injecting malicious code is the most prevalent manner by which XSS is exploited; for this reason, escaping characters in order to prevent this manipulation is the top method for securing code against this vulnerability.

Escaping means that the application is coded to mark key characters, and particularly key characters included in user input, to prevent those characters from being interpreted in a dangerous context. For example, in HTML, < can be coded as &lt; and > can be coded as &gt; in order to be interpreted and displayed as themselves in text, while within the code itself, they are used for HTML tags. If malicious content is injected into an application that escapes special characters and that malicious content uses < and > as HTML tags, those characters are nonetheless not interpreted as HTML tags by the browser if they’ve been correctly escaped in the application code and in this way the attempted attack is diverted.

The most prominent use of XSS is to steal cookies (source: OWASP HttpOnly) and hijack user sessions, but XSS exploits have been used to expose sensitive information, enable access to privileged services and functionality and deliver malware.

Types of attacks

There are a few methods by which XSS can be manipulated:

Affected environments

The following environments are susceptible to an XSS attack:

• Web servers
• Application servers
• Web application environments

How to prevent

This section describes the top best practices designed to specifically protect your code:

• Sanitize data input in an HTTP request before reflecting it back, ensuring all data is validated, filtered or escaped before echoing anything back to the user, such as the values of query parameters during searches.
• Convert special characters such as ?, &, /, <, > and spaces to their respective HTML or URL encoded equivalents.
• Give users the option to disable client-side scripts.
• Redirect invalid requests.
• Detect simultaneous logins, including those from two separate IP addresses, and invalidate those sessions.
• Use and enforce a Content Security Policy (source: Wikipedia) to disable any features that might be manipulated for an XSS attack.
• Read the documentation for any of the libraries referenced in your code to understand which elements allow for embedded HTML.

Remediation

There is no fixed version for node-sass.

References

• GitHub Commit
• NVD

Overview

angular is a package that lets you write client-side web applications as if you had a smarter browser. It also lets you use HTML as your template language and lets you extend HTML’s syntax to express your application’s components clearly and succinctly.

Affected versions of this package are vulnerable to Cross-site Scripting (XSS). XSS may be triggered in AngularJS applications that sanitize user-controlled HTML snippets before passing them to JQLite methods like JQLite.prepend, JQLite.after, JQLite.append, JQLite.replaceWith, JQLite.append, new JQLite and angular.element.

JQLite (DOM manipulation library that's part of AngularJS) manipulates input HTML before inserting it to the DOM in jqLiteBuildFragment.

One of the modifications performed expands an XHTML self-closing tag.

If jqLiteBuildFragment is called (e.g. via new JQLite(aString)) with user-controlled HTML string that was sanitized (e.g. with DOMPurify), the transformation done by JQLite may modify some forms of an inert, sanitized payload into a payload containing JavaScript - and trigger an XSS when the payload is inserted into DOM.

PoC

const inertPayload = `<div><style><style/><img src=x onerror="alert(1337)"/>` 

Note that the style element is not closed and <img would be a text node inside the style if inserted into the DOM as-is. As such, some HTML sanitizers would leave the <img as is without processing it and stripping the onerror attribute.

angular.element(document).append(inertPayload);

This will alert, as <style/> will be replaced with <style></style> before adding it to the DOM, closing the style element early and reactivating img.

Details

Cross-site scripting (or XSS) is a code vulnerability that occurs when an attacker “injects” a malicious script into an otherwise trusted website. The injected script gets downloaded and executed by the end user’s browser when the user interacts with the compromised website.

This is done by escaping the context of the web application; the web application then delivers that data to its users along with other trusted dynamic content, without validating it. The browser unknowingly executes malicious script on the client side (through client-side languages; usually JavaScript or HTML) in order to perform actions that are otherwise typically blocked by the browser’s Same Origin Policy.

Injecting malicious code is the most prevalent manner by which XSS is exploited; for this reason, escaping characters in order to prevent this manipulation is the top method for securing code against this vulnerability.

Escaping means that the application is coded to mark key characters, and particularly key characters included in user input, to prevent those characters from being interpreted in a dangerous context. For example, in HTML, < can be coded as &lt; and > can be coded as &gt; in order to be interpreted and displayed as themselves in text, while within the code itself, they are used for HTML tags. If malicious content is injected into an application that escapes special characters and that malicious content uses < and > as HTML tags, those characters are nonetheless not interpreted as HTML tags by the browser if they’ve been correctly escaped in the application code and in this way the attempted attack is diverted.

The most prominent use of XSS is to steal cookies (source: OWASP HttpOnly) and hijack user sessions, but XSS exploits have been used to expose sensitive information, enable access to privileged services and functionality and deliver malware.

Types of attacks

There are a few methods by which XSS can be manipulated:

Affected environments

The following environments are susceptible to an XSS attack:

• Web servers
• Application servers
• Web application environments

How to prevent

This section describes the top best practices designed to specifically protect your code:

• Sanitize data input in an HTTP request before reflecting it back, ensuring all data is validated, filtered or escaped before echoing anything back to the user, such as the values of query parameters during searches.
• Convert special characters such as ?, &, /, <, > and spaces to their respective HTML or URL encoded equivalents.
• Give users the option to disable client-side scripts.
• Redirect invalid requests.
• Detect simultaneous logins, including those from two separate IP addresses, and invalidate those sessions.
• Use and enforce a Content Security Policy (source: Wikipedia) to disable any features that might be manipulated for an XSS attack.
• Read the documentation for any of the libraries referenced in your code to understand which elements allow for embedded HTML.

Remediation

Upgrade angular to version 1.8.0 or higher.

References

• GitHub Commit

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to improper input sanitization. An attacker can increase the CPU usage and crash the program by crafting a very large and well crafted string.

PoC

const { argument } = require('cross-spawn/lib/util/escape');
var str = "";
for (var i = 0; i < 1000000; i++) {
  str += "\\";
}
str += "◎";

console.log("start")
argument(str)
console.log("end")

// run `npm install cross-spawn` and `node attack.js` 
// then the program will stuck forever with high CPU usage

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade cross-spawn to version 6.0.6, 7.0.5 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub PR

Overview

Affected versions of this package are vulnerable to Denial of Service (DoS) due to an UnhandledPromiseRejection error thrown by micromatch. An attacker could kill the Node.js process and crash the server by making requests to certain paths.

PoC

1. Run a server like this:

const express = require('express')
const { createProxyMiddleware } = require('http-proxy-middleware')

const frontend = express()
frontend.use(createProxyMiddleware({
  target: 'http://localhost:3031',
  pathFilter: '*'
}))
frontend.listen(3030)

const backend = express()
backend.use((req, res) => res.send('ok'))
backend.listen(3031)

2. curl 'localhost:3030//x@x'

Expected: Response with payload ok

Actual: Server crashes with error TypeError: Expected input to be a string (from micromatch)

On v1 and v2 of http-proxy-middleware, it's also possible to exclude pathFilter and cause the server to crash with TypeError: Cannot read properties of null (reading 'indexOf') (from matchSingleStringPath).

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade http-proxy-middleware to version 2.0.7, 3.0.3 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Gist

Overview

tar is a full-featured Tar for Node.js.

Affected versions of this package are vulnerable to Arbitrary File Write. node-tar aims to guarantee that any file whose location would be modified by a symbolic link is not extracted. This is, in part, achieved by ensuring that extracted directories are not symlinks. Additionally, in order to prevent unnecessary stat calls to determine whether a given path is a directory, paths are cached when directories are created.

This logic was insufficient when extracting tar files that contained both a directory and a symlink with the same name as the directory, where the symlink and directory names in the archive entry used backslashes as a path separator on posix systems. The cache checking logic used both \ and / characters as path separators. However, \ is a valid filename character on posix systems.

By first creating a directory, and then replacing that directory with a symlink, it is possible to bypass node-tar symlink checks on directories, essentially allowing an untrusted tar file to symlink into an arbitrary location. This can lead to extracting arbitrary files into that location, thus allowing arbitrary file creation and overwrite.

Additionally, a similar confusion could arise on case-insensitive filesystems. If a tar archive contained a directory at FOO, followed by a symbolic link named foo, then on case-insensitive file systems, the creation of the symbolic link would remove the directory from the filesystem, but not from the internal directory cache, as it would not be treated as a cache hit. A subsequent file entry within the FOO directory would then be placed in the target of the symbolic link, thinking that the directory had already been created.

Remediation

Upgrade tar to version 6.1.7, 5.0.8, 4.4.16 or higher.

References

• GitHub Commit

Overview

tar is a full-featured Tar for Node.js.

Affected versions of this package are vulnerable to Arbitrary File Write. node-tar aims to guarantee that any file whose location would be modified by a symbolic link is not extracted. This is, in part, achieved by ensuring that extracted directories are not symlinks. Additionally, in order to prevent unnecessary stat calls to determine whether a given path is a directory, paths are cached when directories are created.

This logic is insufficient when extracting tar files that contain two directories and a symlink with names containing unicode values that normalized to the same value. Additionally, on Windows systems, long path portions would resolve to the same file system entities as their 8.3 "short path" counterparts. A specially crafted tar archive can include directories with two forms of the path that resolve to the same file system entity, followed by a symbolic link with a name in the first form, lastly followed by a file using the second form. This leads to bypassing node-tar symlink checks on directories, essentially allowing an untrusted tar file to symlink into an arbitrary location and extracting arbitrary files into that location.

Remediation

Upgrade tar to version 6.1.9, 5.0.10, 4.4.18 or higher.

References

• GitHub Commit

Overview

tar is a full-featured Tar for Node.js.

Affected versions of this package are vulnerable to Arbitrary File Write. node-tar aims to guarantee that any file whose location would be outside of the extraction target directory is not extracted. This is, in part, accomplished by sanitizing absolute paths of entries within the archive, skipping archive entries that contain .. path portions, and resolving the sanitized paths against the extraction target directory.

This logic is insufficient on Windows systems when extracting tar files that contain a path that is not an absolute path, but specify a drive letter different from the extraction target, such as C:some\path. If the drive letter does not match the extraction target, for example D:\extraction\dir, then the result of path.resolve(extractionDirectory, entryPath) resolves against the current working directory on the C: drive, rather than the extraction target directory.

Additionally, a .. portion of the path can occur immediately after the drive letter, such as C:../foo, and is not properly sanitized by the logic that checks for .. within the normalized and split portions of the path.

Note: This only affects users of node-tar on Windows systems.

Remediation

Upgrade tar to version 6.1.9, 5.0.10, 4.4.18 or higher.

References

• GitHub Commit

Overview

open is a cross platform package that opens stuff like URLs, files, executables.

Affected versions of this package are vulnerable to Arbitrary Command Injection. Urls are not properly escaped before concatenating them into the command that is opened using exec().

Note: Upgrading open to the last version will prevent this vulnerability but is also likely to have unwanted effects since it now has a very different API.

Remediation

Upgrade open to version 6.0.0 or higher.

References

• HackerOne Report

Overview

tar is a full-featured Tar for Node.js.

Affected versions of this package are vulnerable to Arbitrary File Overwrite. This is due to insufficient symlink protection. node-tar aims to guarantee that any file whose location would be modified by a symbolic link is not extracted. This is, in part, achieved by ensuring that extracted directories are not symlinks. Additionally, in order to prevent unnecessary stat calls to determine whether a given path is a directory, paths are cached when directories are created.

This logic is insufficient when extracting tar files that contain both a directory and a symlink with the same name as the directory. This order of operations results in the directory being created and added to the node-tar directory cache. When a directory is present in the directory cache, subsequent calls to mkdir for that directory are skipped. However, this is also where node-tar checks for symlinks occur. By first creating a directory, and then replacing that directory with a symlink, it is possible to bypass node-tar symlink checks on directories, essentially allowing an untrusted tar file to symlink into an arbitrary location and subsequently extracting arbitrary files into that location.

Remediation

Upgrade tar to version 3.2.3, 4.4.15, 5.0.7, 6.1.2 or higher.

References

• GitHub Commit

Overview

tar is a full-featured Tar for Node.js.

Affected versions of this package are vulnerable to Arbitrary File Overwrite. This is due to insufficient absolute path sanitization.

node-tar aims to prevent extraction of absolute file paths by turning absolute paths into relative paths when the preservePaths flag is not set to true. This is achieved by stripping the absolute path root from any absolute file paths contained in a tar file. For example, the path /home/user/.bashrc would turn into home/user/.bashrc.

This logic is insufficient when file paths contain repeated path roots such as ////home/user/.bashrc. node-tar only strips a single path root from such paths. When given an absolute file path with repeating path roots, the resulting path (e.g. ///home/user/.bashrc) still resolves to an absolute path.

Remediation

Upgrade tar to version 3.2.2, 4.4.14, 5.0.6, 6.1.1 or higher.

References

• GitHub Commit

Overview

ajv is an Another JSON Schema Validator

Affected versions of this package are vulnerable to Prototype Pollution. A carefully crafted JSON schema could be provided that allows execution of other code by prototype pollution. (While untrusted schemas are recommended against, the worst case of an untrusted schema should be a denial of service, not execution of code.)

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

1. Freeze the prototype— use Object.freeze (Object.prototype).

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade ajv to version 6.12.3 or higher.

References

• HackerOne Report

Overview

engine.io-client, the client for engine.io and socket.io, disables the core SSL/TLS verification checks by default.

This allows an active attacker, for instance one operating a malicious WiFi, to intercept these encrypted connections using the attacker's spoofed certificate and keys. Doing so compromises the data communicated over this channel, as well as allowing an attacker to impersonate both the server and the client during the live session, sending spoofed data to either side.

Remediation

Update to version 1.6.9 or greater.

If a direct dependency update is not possible, use snyk wizard to patch this vulnerability.

References

• Cigital Blog
• GitHub Commit

Overview

js-yaml is a human-friendly data serialization language.

Affected versions of this package are vulnerable to Arbitrary Code Execution. When an object with an executable toString() property used as a map key, it will execute that function. This happens only for load(), which should not be used with untrusted data anyway. safeLoad() is not affected because it can't parse functions.

Remediation

Upgrade js-yaml to version 3.13.1 or higher.

References

• GitHub Commit
• GitHub PR
• NPM Security Advisory

Overview

angular is a package that lets you write client-side web applications as if you had a smarter browser. It also lets you use HTML as your template language and lets you extend HTML’s syntax to express your application’s components clearly and succinctly.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). A regular expression used to split the value of the ng-srcset directive is vulnerable to super-linear runtime due to backtracking. With large carefully-crafted input, this can result in catastrophic backtracking and cause a denial of service.

Note:

This package is EOL and will not receive any updates to address this issue. Users should migrate to @angular/core.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

There is no fixed version for angular.

References

• PoC

Overview

braces is a Bash-like brace expansion, implemented in JavaScript.

Affected versions of this package are vulnerable to Excessive Platform Resource Consumption within a Loop due improper limitation of the number of characters it can handle, through the parse function. An attacker can cause the application to allocate excessive memory and potentially crash by sending imbalanced braces as input.

PoC

const { braces } = require('micromatch');

console.log("Executing payloads...");

const maxRepeats = 10;

for (let repeats = 1; repeats <= maxRepeats; repeats += 1) {
  const payload = '{'.repeat(repeats*90000);

  console.log(`Testing with ${repeats} repeats...`);
  const startTime = Date.now();
  braces(payload);
  const endTime = Date.now();
  const executionTime = endTime - startTime;
  console.log(`Regex executed in ${executionTime / 1000}s.\n`);
} 

Remediation

Upgrade braces to version 3.0.3 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR
• Vulnerable Code

Overview

ecstatic is a simple static file server middleware. Use it with a raw http server, express/connect or on the CLI.

Affected versions of this package are vulnerable to Denial of Service (DoS). It is possible to crash a server using the package due to the way URL params parsing is handled during redirect.

PoC

curl --path-as-is $(echo -e -n "http://127.0.0.1:8080/existing-dir-name?\x0cfoo")

In the PoC the library is trying to redirect /existing-dir-name?\x0cfoo to /existing-dir-name/?\x0cfoo which cause TypeError: The header content contains invalid characters error because of \x0c symbol.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade ecstatic to version 4.1.4 or higher.

References

• GitHub Commit

Overview

engine.io is a realtime engine behind Socket.IO. It provides the foundation of a bidirectional connection between client and server

Affected versions of this package are vulnerable to Denial of Service (DoS) via a POST request to the long polling transport.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade engine.io to version 3.6.0 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Tags
• PoC
• Research Blogpost

Overview

engine.io is a realtime engine behind Socket.IO. It provides the foundation of a bidirectional connection between client and server

Affected versions of this package are vulnerable to Denial of Service (DoS). A malicious client could send a specially crafted HTTP request, triggering an uncaught exception and killing the Node.js process.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade engine.io to version 3.6.1, 6.2.1 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub PR

Overview

Affected versions of this package are vulnerable to Prototype Pollution in parseQuery function via the name variable in parseQuery.js. This pollutes the prototype of the object returned by parseQuery and not the global Object prototype (which is the commonly understood definition of Prototype Pollution). Therefore, the actual impact will depend on how applications utilize the returned object and how they filter unwanted keys.

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

1. Freeze the prototype— use Object.freeze (Object.prototype).

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade loader-utils to version 1.4.1, 2.0.3 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Issue
• GitHub Issue
• GitHub PR
• GitHub Release
• GitHub Release Notes
• Vulnerable Code
• Vulnerable Code

Overview

lodash is a modern JavaScript utility library delivering modularity, performance, & extras.

Affected versions of this package are vulnerable to Prototype Pollution through the zipObjectDeep function due to improper user input sanitization in the baseZipObject function.

PoC

lodash.zipobjectdeep:

const zipObjectDeep = require("lodash.zipobjectdeep");

let emptyObject = {};


console.log(`[+] Before prototype pollution : ${emptyObject.polluted}`);
//[+] Before prototype pollution : undefined

zipObjectDeep(["constructor.prototype.polluted"], [true]);
//we inject our malicious attributes in the vulnerable function

console.log(`[+] After prototype pollution : ${emptyObject.polluted}`);
//[+] After prototype pollution : true

lodash:

const test = require("lodash");

let emptyObject = {};


console.log(`[+] Before prototype pollution : ${emptyObject.polluted}`);
//[+] Before prototype pollution : undefined

test.zipObjectDeep(["constructor.prototype.polluted"], [true]);
//we inject our malicious attributes in the vulnerable function

console.log(`[+] After prototype pollution : ${emptyObject.polluted}`);
//[+] After prototype pollution : true

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

1. Freeze the prototype— use Object.freeze (Object.prototype).

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade lodash to version 4.17.17 or higher.

References

• GitHub Gist
• Vulnerable Code

Overview

minimatch is a minimal matching utility.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via complicated and illegal regexes.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade minimatch to version 3.0.2 or higher.

References

• GitHub Commit

Overview

minimatch is a minimal matching utility.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS).

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade minimatch to version 3.0.2 or higher.

References

• GitHub Commit

Overview

mocha is a javascript test framework for node.js & the browser.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in the clean function in utils.js.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade mocha to version 10.1.0 or higher.

References

• GitHub PR
• GitHub Release

Overview

mocha is a javascript test framework for node.js & the browser.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). If the stack trace in utils.js begins with a large error message (>= 20k characters), and full-trace is not undisabled, utils.stackTraceFilter() will take exponential time to run.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade mocha to version 6.0.0 or higher.

References

• GitHub Commit
• GitHub PR

Overview

parsejson is a method that parses a JSON string and returns a JSON object.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) attacks. An attacker may pass a specially crafted JSON data, causing the server to hang.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

There is no fixed version for parsejson.

References

• GitHub Issue

Overview

qs is a querystring parser that supports nesting and arrays, with a depth limit.

Affected versions of this package are vulnerable to Prototype Override Protection Bypass. By default qs protects against attacks that attempt to overwrite an object's existing prototype properties, such as toString(), hasOwnProperty(),etc.

From qs documentation:

By default parameters that would overwrite properties on the object prototype are ignored, if you wish to keep the data from those fields either use plainObjects as mentioned above, or set allowPrototypes to true which will allow user input to overwrite those properties. WARNING It is generally a bad idea to enable this option as it can cause problems when attempting to use the properties that have been overwritten. Always be careful with this option.

Overwriting these properties can impact application logic, potentially allowing attackers to work around security controls, modify data, make the application unstable and more.

In versions of the package affected by this vulnerability, it is possible to circumvent this protection and overwrite prototype properties and functions by prefixing the name of the parameter with [ or ]. e.g. qs.parse("]=toString") will return {toString = true}, as a result, calling toString() on the object will throw an exception.

Example:

qs.parse('toString=foo', { allowPrototypes: false })
// {}

qs.parse("]=toString", { allowPrototypes: false })
// {toString = true} <== prototype overwritten

For more information, you can check out our blog.

Disclosure Timeline

• February 13th, 2017 - Reported the issue to package owner.
• February 13th, 2017 - Issue acknowledged by package owner.
• February 16th, 2017 - Partial fix released in versions 6.0.3, 6.1.1, 6.2.2, 6.3.1.
• March 6th, 2017 - Final fix released in versions 6.4.0,6.3.2, 6.2.3, 6.1.2 and 6.0.4

Remediation

Upgrade qs to version 6.0.4, 6.1.2, 6.2.3, 6.3.2 or higher.

References

• GitHub Commit
• GitHub Issue

Overview

qs is a querystring parser that supports nesting and arrays, with a depth limit.

Affected versions of this package are vulnerable to Prototype Poisoning which allows attackers to cause a Node process to hang, processing an Array object whose prototype has been replaced by one with an excessive length value.

Note: In many typical Express use cases, an unauthenticated remote attacker can place the attack payload in the query string of the URL that is used to visit the application, such as a[__proto__]=b&a[__proto__]&a[length]=100000000.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade qs to version 6.2.4, 6.3.3, 6.4.1, 6.5.3, 6.6.1, 6.7.3, 6.8.3, 6.9.7, 6.10.3 or higher.

References

• GitHub PR
• RedHat Bugzilla Bug
• Researcher Advisory

Overview

semver is a semantic version parser used by npm.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the function new Range, when untrusted user data is provided as a range.

PoC

const semver = require('semver')
const lengths_2 = [2000, 4000, 8000, 16000, 32000, 64000, 128000]

console.log("n[+] Valid range - Test payloads")
for (let i = 0; i =1.2.3' + ' '.repeat(lengths_2[i]) + '<1.3.0';
const start = Date.now()
semver.validRange(value)
// semver.minVersion(value)
// semver.maxSatisfying(["1.2.3"], value)
// semver.minSatisfying(["1.2.3"], value)
// new semver.Range(value, {})

const end = Date.now();
console.log('length=%d, time=%d ms', value.length, end - start);
}

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade semver to version 5.7.2, 6.3.1, 7.5.2 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub PR
• Vulnerable Code
• Vulnerable Code
• Vulnerable Code

Overview

socket.io-parser is a socket.io protocol parser

Affected versions of this package are vulnerable to Denial of Service (DoS) via a large packet because a concatenation approach is used.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade socket.io-parser to version 3.3.2, 3.4.1 or higher.

References

• GitHub Commit
• PoC
• Research Blogpost

Overview

trim-newlines is a Trim newlines from the start and/or end of a string

Affected versions of this package are vulnerable to Denial of Service (DoS) via the end() method.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade trim-newlines to version 3.0.1, 4.0.1 or higher.

References

• GitHub Commit
• GitHub Release

Overview

Affected versions of this package are vulnerable to Prototype Pollution via the unset function in index.js, because it allows access to object prototype properties.

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

1. Freeze the prototype— use Object.freeze (Object.prototype).

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade unset-value to version 2.0.1 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR
• GitHub Release

Overview

useragent allows you to parse user agent string with high accuracy by using hand tuned dedicated regular expressions for browser matching.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) when passing long user-agent strings.

This is due to incomplete fix for this vulnerability: https://snyk.io/vuln/SNYK-JS-USERAGENT-11000.

An attempt to fix the vulnerability has been pushed to master.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

A fix was pushed into the master branch but not yet published.

References

• GitHub Commit

• GitHub PR

• GitHub PR

• GitHub PR

• Hackerone Report

Overview

webpack-dev-server Uses webpack with a development server that provides live reloading. It should be used for development only.

Affected versions of this package are vulnerable to Information Exposure. The origin of requests is not checked by the WebSocket server, which is used for HMR. A malicious user could receive the HMR message sent by the WebSocket server via a ws://127.0.0.1:8080/ connection from any origin.

Remediation

Upgrade webpack-dev-server to version 3.1.11 or higher.

References

• GitHub Commit

• GitHub Issue

• NPM Security Advisory

• POC: Potential Exploit

Overview

ws is a WebSocket client and server implementation.

Affected versions of this package did not limit the size of an incoming payload before it was processed by default. As a result, a very large payload (over 256MB in size) could lead to a failed allocation and crash the node process - enabling a Denial of Service attack.

While 256MB may seem excessive, note that the attack is likely to be sent from another server, not an end-user computer, using data-center connection speeds. In those speeds, a payload of this size can be transmitted in seconds.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Update to version 1.1.1 or greater, which sets a default maxPayload of 100MB. If you cannot upgrade, apply a Snyk patch, or provide ws with options setting the maxPayload to an appropriate size that is smaller than 256MB.

References

• WS Issue
• Resulting crash in Node

Overview

ws is a simple to use websocket client, server and console for node.js.

Affected versions of this package are vulnerable to Denial of Service (DoS) attacks. A specially crafted value of the Sec-WebSocket-Extensions header that used Object.prototype property names as extension or parameter names could be used to make a ws server crash.

PoC:

const WebSocket = require('ws');
const net = require('net');

const wss = new WebSocket.Server({ port: 3000 }, function () {
  const payload = 'constructor';  // or ',;constructor'

  const request = [
    'GET / HTTP/1.1',
    'Connection: Upgrade',
    'Sec-WebSocket-Key: test',
    'Sec-WebSocket-Version: 8',
    `Sec-WebSocket-Extensions: ${payload}`,
    'Upgrade: websocket',
    '\r\n'
  ].join('\r\n');

  const socket = net.connect(3000, function () {
    socket.resume();
    socket.write(request);
  });
});

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.

Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.

One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.

When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.

Two common types of DoS vulnerabilities:

• High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.

• Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm ws package

Remediation

Upgrade ws to version 1.1.5, 3.3.1 or higher.

References

• GitHub Commit

• GitHub Commit

• GitHub Release

Overview

hawk is a library for the HTTP Hawk Authentication Scheme.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in header parsing where each added character in the attacker's input increases the computation time exponentially.

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade hawk to version 9.0.1 or higher.

References

• GitHub Commit
• GitHub PR

Overview

Affected versions of this package are vulnerable to Path Traversal due to insufficient validation of the supplied URL address before returning the local file. This issue allows accessing any file on the developer's machine. The middleware can operate with either the physical filesystem or a virtualized in-memory memfs filesystem. When the writeToDisk configuration option is set to true, the physical filesystem is utilized. The getFilenameFromUrl method parses the URL and constructs the local file path by stripping the public path prefix from the URL and appending the unescaped path suffix to the outputPath. Since the URL is not unescaped and normalized automatically before calling the middleware, it is possible to use %2e and %2f sequences to perform a path traversal attack.

Notes:

1. This vulnerability is exploitable without any specific configurations, allowing an attacker to access and exfiltrate content from any file on the developer's machine.

2. If the development server is exposed on a public IP address or 0.0.0.0, an attacker on the local network can access the files without victim interaction.

3. If the server permits access from third-party domains, a malicious link could lead to local file exfiltration when visited by the victim.

PoC

A blank project can be created containing the following configuration file webpack.config.js:

module.exports = { devServer: { devMiddleware: { writeToDisk: true } } };

When started, it is possible to access any local file, e.g. /etc/passwd:

$ curl localhost:8080/public/..%2f..%2f..%2f..%2f../etc/passwd

root:x:0:0:root:/root:/bin/bash
daemon:x:1:1:daemon:/usr/sbin:/usr/sbin/nologin
bin:x:2:2:bin:/bin:/usr/sbin/nologin
sys:x:3:3:sys:/dev:/usr/sbin/nologin
sync:x:4:65534:sync:/bin:/bin/sync
games:x:5:60:games:/usr/games:/usr/sbin/nologin

Remediation

Upgrade webpack-dev-middleware to version 5.3.4, 6.1.2, 7.1.0 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub Release
• GitHub Release
• GitHub Release
• Vulnerable Code
• Vulnerable Code

Overview

angular is a package that lets you write client-side web applications as if you had a smarter browser. It also lets you use HTML as your template language and lets you extend HTML’s syntax to express your application’s components clearly and succinctly.

Affected versions of this package are vulnerable to Prototype Pollution. The function merge() could be tricked into adding or modifying properties of Object.prototype using a __proto__ payload.

PoC by Snyk

angular.merge({}, JSON.parse('{"__proto__": {"xxx": "polluted"}}'));
console.log(({}).xxx);

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

1. Freeze the prototype— use Object.freeze (Object.prototype).

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade angular to version 1.7.9 or higher.

References

• GitHub Commit

Overview

lodash is a modern JavaScript utility library delivering modularity, performance, & extras.

Affected versions of this package are vulnerable to Prototype Pollution. The function defaultsDeep could be tricked into adding or modifying properties of Object.prototype using a constructor payload.

PoC by Snyk

const mergeFn = require('lodash').defaultsDeep;
const payload = '{"constructor": {"prototype": {"a0": true}}}'

function check() {
    mergeFn({}, JSON.parse(payload));
    if (({})[`a0`] === true) {
        console.log(`Vulnerable to Prototype Pollution via ${payload}`);
    }
  }

check();

For more information, check out our blog post

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

1. Freeze the prototype— use Object.freeze (Object.prototype).

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade lodash to version 4.17.12 or higher.

References

• GitHub Issue
• GitHub PR
• GitHub PR
• GitHub PR
• Node Security Advisory
• Snyk Blog

Overview

lodash is a modern JavaScript utility library delivering modularity, performance, & extras.

Affected versions of this package are vulnerable to Prototype Pollution via the set and setwith functions due to improper user input sanitization.

PoC

lod = require('lodash')
lod.set({}, "__proto__[test2]", "456")
console.log(Object.prototype)

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

1. Freeze the prototype— use Object.freeze (Object.prototype).

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade lodash to version 4.17.17 or higher.

References

• HackerOne Report

Overview

lodash is a modern JavaScript utility library delivering modularity, performance, & extras.

Affected versions of this package are vulnerable to Prototype Pollution. The functions merge, mergeWith, and defaultsDeep could be tricked into adding or modifying properties of Object.prototype. This is due to an incomplete fix to CVE-2018-3721.

Details

Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__, constructor and prototype. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.

There are two main ways in which the pollution of prototypes occurs:

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

The logic of a vulnerable recursive merge function follows the following high-level model:

merge (target, source)

  foreach property of source

    if property exists and is an object on both the target and the source

      merge(target[property], source[property])

    else

      target[property] = source[property]

When the source object contains a property named __proto__ defined with Object.defineProperty() , the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object and the source of Object as defined by the attacker. Properties are then copied on the Object prototype.

Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source).

lodash and Hoek are examples of libraries susceptible to recursive merge attacks.

Property definition by path

There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)

If the attacker can control the value of “path”, they can set this value to __proto__.myValue. myValue is then assigned to the prototype of the class of the object.

Types of attacks

There are a few methods by which Prototype Pollution can be manipulated:

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

1. Freeze the prototype— use Object.freeze (Object.prototype).

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

4. Consider using objects without prototypes (for example, Object.create(null)), breaking the prototype chain and preventing pollution.

5. As a best practice use Map instead of Object.

For more information on this vulnerability type:

Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018

Remediation

Upgrade lodash to version 4.17.11 or higher.

References

• GitHub Commit
• GitHub PR
• NPM Security Advisory
• NPM Security Advisory
• NPM Security Advisory
• NPM Security Advisory
• POC: HackerOne Report

Overview

lodash is a modern JavaScript utility library delivering modularity, performance, & extras.

Affected versions of this package are vulnerable to Code Injection via template.

PoC

var _ = require('lodash');

_.template('', { variable: '){console.log(process.env)}; with(obj' })()

Remediation

Upgrade lodash to version 4.17.21 or higher.

References

• GitHub Commit
• Vulnerable Code

Overview

lodash.template is a The Lodash method _.template exported as a Node.js module.

Affected versions of this package are vulnerable to Code Injection via template.

PoC

var _ = require('lodash');

_.template('', { variable: '){console.log(process.env)}; with(obj' })()

Remediation

There is no fixed version for lodash.template.

References

• GitHub Commit
• Vulnerable Code

Overview

axios is a promise-based HTTP client for the browser and Node.js.

Affected versions of this package are vulnerable to Cross-site Request Forgery (CSRF) due to inserting the X-XSRF-TOKEN header using the secret XSRF-TOKEN cookie value in all requests to any server when the XSRF-TOKEN0 cookie is available, and the withCredentials setting is turned on. If a malicious user manages to obtain this value, it can potentially lead to the XSRF defence mechanism bypass.

Workaround

Users should change the default XSRF-TOKEN cookie name in the Axios configuration and manually include the corresponding header only in the specific places where it's necessary.

Remediation

Upgrade axios to version 0.28.0, 1.6.0 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub Issue
• GitHub PR

Overview

diff is a javascript text differencing implementation.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). This can cause an impact of about 10 seconds matching time for data 48K characters long.

Disclosure Timeline

• Feb 15th, 2018 - Initial Disclosure to package owner
• Feb 16th, 2018 - Initial Response from package owner
• Mar 5th, 2018 - Fix issued
• Mar 6th, 2018 - Vulnerability published

Details

Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its original and legitimate users. There are many types of DoS attacks, ranging from trying to clog the network pipes to the system by generating a large volume of traffic from many machines (a Distributed Denial of Service - DDoS - attack) to sending crafted requests that cause a system to crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service attack. Regular expressions are incredibly powerful, but they aren't very intuitive and can ultimately end up making it easy for attackers to take your site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/

This regular expression accomplishes the following:

• A The string must start with the letter 'A'
• (B|C+)+ The string must then follow the letter A with either the letter 'B' or some number of occurrences of the letter 'C' (the + matches one or more times). The + at the end of this section states that we can look for one or more matches of this section.
• D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total

The entire process of testing it against a 30 characters long string takes around ~52ms. But when given an invalid string, it takes nearly two seconds to complete the test, over ten times as long as it took to test a valid string. The dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine will match the first possible way to accept the current character and proceed to the next one. If it then fails to match the next one, it will backtrack and see if there was another way to digest the previous character. If it goes too far down the rabbit hole only to find out the string doesn’t match in the end, and if many characters have multiple valid regex paths, the number of backtracking steps can become very large, resulting in what is known as catastrophic backtracking.

Let's look at how our expression runs into this problem, using a shorter string: "ACCCX". While it seems fairly straightforward, there are still four different ways that the engine could match those three C's:

1. CCC
2. CC+C
3. C+CC
4. C+C+C.

The engine has to try each of those combinations to see if any of them potentially match against the expression. When you combine that with the other steps the engine must take, we can use RegEx 101 debugger to see the engine has to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just continues to grow.

By the time the string includes 14 C's, the engine has to take over 65,000 steps just to see if the string is valid. These extreme situations can cause them to work very slowly (exponentially related to input size, as shown above), allowing an attacker to exploit this and can cause the service to excessively consume CPU, resulting in a Denial of Service.

Remediation

Upgrade diff to version 3.5.0 or higher.

References

• GitHub Commit

Overview

shelljs is a wrapper for the Unix shell commands for Node.js.

Affected versions of this package are vulnerable to Improper Privilege Management. When ShellJS is used to create shell scripts which may be running as root, users with low-level privileges on the system can leak sensitive information such as passwords (depending on implementation) from the standard output of the privileged process OR shutdown privileged ShellJS processes via the exec function when triggering EACCESS errors.

Note: Thi only impacts the synchronous version of shell.exec().

Remediation

Upgrade shelljs to version 0.8.5 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR

Overview

webpack-dev-server is an Uses webpack with a development server that provides live reloading. It should be used for development only.

Affected versions of this package are vulnerable to Origin Validation Error via theOrigin header, which allows IP address origins to connect to WebSocket in the checkHeader function. An attacker can obtain sensitive data when accessing a malicious website with a non-Chromium-based browser by exploiting the WebSocket connection.

Note: Chrome 94+ (and other Chromium-based browsers) users are unaffected by this vulnerability due to the non-HTTPS private access blocking feature.

Remediation

Upgrade webpack-dev-server to version 5.2.1 or higher.

References

• GitHub Commit
• GitHub Release
• Vulnerable Code