@aaa-backend-stack/storage@1.16.9

Vulnerabilities

26 via 141 paths

Dependencies

442

Source

npm

Find, fix and prevent vulnerabilities in your code.

Severity
  • 1
  • 8
  • 14
  • 3
Status
  • 26
  • 0
  • 0

critical severity

Prototype Pollution

  • Vulnerable module: lodash
  • Introduced through: @aaa-backend-stack/build-tools@1.16.9, @aaa-backend-stack/logger@1.16.9 and others

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

Affected versions of this package are vulnerable to Prototype Pollution in zipObjectDeep due to an incomplete fix for CVE-2020-8203.

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 lodash to version 4.17.20 or higher.

References

high severity

Command Injection

  • Vulnerable module: lodash
  • Introduced through: @aaa-backend-stack/build-tools@1.16.9, @aaa-backend-stack/logger@1.16.9 and others

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

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

PoC

var _ = require('lodash');

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

Remediation

Upgrade lodash to version 4.17.21 or higher.

References

high severity

Prototype Pollution

  • Vulnerable module: lodash
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

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

PoC by Snyk

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

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

check();

For more information, check out our blog post

Details

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

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

  • Unsafe Object recursive merge
  • Property definition by path

Unsafe Object recursive merge

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

merge (target, source)

  foreach property of source

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

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

    else

      target[property] = source[property]

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

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

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

Property definition by path

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

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

Types of attacks

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

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 lodash to version 4.17.12 or higher.

References

high severity

Prototype Pollution

  • Vulnerable module: lodash
  • Introduced through: @aaa-backend-stack/build-tools@1.16.9, @aaa-backend-stack/logger@1.16.9 and others

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

Affected versions of this package are vulnerable to Prototype Pollution via the setWith and set functions.

PoC by awarau

  • Create a JS file with this contents:
    lod = require('lodash')
    lod.setWith({}, "__proto__[test]", "123")
    lod.set({}, "__proto__[test2]", "456")
    console.log(Object.prototype)
    
  • Execute it with node
  • Observe that test and test2 is now in the Object.prototype.

Details

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

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

  • Unsafe Object recursive merge
  • Property definition by path

Unsafe Object recursive merge

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

merge (target, source)

  foreach property of source

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

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

    else

      target[property] = source[property]

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

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

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

Property definition by path

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

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

Types of attacks

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

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 lodash to version 4.17.17 or higher.

References

high severity

Prototype Pollution

  • Vulnerable module: lodash
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

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

Details

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

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

  • Unsafe Object recursive merge
  • Property definition by path

Unsafe Object recursive merge

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

merge (target, source)

  foreach property of source

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

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

    else

      target[property] = source[property]

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

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

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

Property definition by path

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

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

Types of attacks

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

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 lodash to version 4.17.11 or higher.

References

high severity

Command Injection

  • Vulnerable module: nodemailer
  • Introduced through: @aaa-backend-stack/logger@1.16.9 and @aaa-backend-stack/utils@1.16.9

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 nodemailer@6.3.1
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 nodemailer@6.3.1

Overview

nodemailer is an Easy as cake e-mail sending from your Node.js applications

Affected versions of this package are vulnerable to Command Injection. Use of crafted recipient email addresses may result in arbitrary command flag injection in sendmail transport for sending mails.

PoC

-bi@example.com (-bi Initialize the alias database.)
-d0.1a@example.com (The option -d0.1 prints the version of sendmail and the options it was compiled with.)
-Dfilename@example.com (Debug output ffile)

Remediation

Upgrade nodemailer to version 6.4.16 or higher.

References

high severity

Hash Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0
    Remediation: Upgrade to sequelize@4.12.0.

Overview

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

Affected versions of this package are vulnerable to Hash Injection. Using specially crafted requests an attacker can bypass secret_token protections on websites using sequalize.

For example:

db.Token.findOne({
      where: {
        token: req.query.token
      }
);

Node.js and other platforms allow nested parameters, i.e. token[$gt]=1 will be transformed into token = {"$gt":1}. When such a hash is passed into sequalize it will consider it a query (greater than 1) and find the first token in the DB, bypassing security of this endpoint.

Remediation

Upgrade sequelize to version 4.12.0 or higher.

References

high severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0
    Remediation: Upgrade to sequelize@3.35.1.

Overview

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

Affected versions of this package are vulnerable to SQL Injection due to JSON path keys not being properly escaped for the MySQL/MariaDB dialects.

PoC by Snyk

const Sequelize = require('sequelize');
const sequelize = new Sequelize('mysql', 'root', 'root', {
  host: 'localhost',
  port: '3306',
  dialect: 'mariadb',
});

class Project extends Sequelize.Model {}

Project.init({
  name: Sequelize.STRING,
  target: Sequelize.JSON,
}, {
  sequelize,
  tableName: 'projects',
});

(async () => {
  await sequelize.sync();

  console.log(await Project.findAll({
    where: {target: {"a')) AS DECIMAL) = 1 UNION SELECT VERSION(); -- ": 1}},
    attributes: ['name'],
    raw: true,
  }));
})();

// https://github.com/sequelize/sequelize/blob/master/lib/dialects/abstract/query-generator.js#L1059-L1061
// case 'mariadb':
//   pathStr = ['$'].concat(paths).join('.');
//   return `json_unquote(json_extract(${quotedColumn},'${pathStr}'))`;

Remediation

Upgrade sequelize to version 3.35.1, 4.44.3, 5.8.11 or higher.

References

high severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0
    Remediation: Upgrade to sequelize@3.35.1.

Overview

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

Affected versions of this package are vulnerable to SQL Injection due to JSON path keys not being properly sanitized in the Postgres dialect.

PoC by Snyk

const Sequelize = require('sequelize');

const sequelize = new Sequelize('someregistry', 'postgres', '', {
  host: 'localhost',
  port: '5432',
  dialect: 'postgres'
});

const Project = sequelize.define('Project', {
  name: Sequelize.DataTypes.TEXT,
  target: Sequelize.DataTypes.JSONB,
}, {
  tableName: 'projects',
});

(async () => {
  await sequelize.authenticate();

  console.log(await Project.findAll({
    where: {target: {"a": 1}},
    attributes: ['name'],
    raw: true
  }));

  console.log(await Project.findAll({
    where: {target: {"a}') = '1' UNION SELECT VERSION(); -- ": 1}},
    attributes: ['name'],
    raw: true
  }));
})();

// https://github.com/sequelize/sequelize/blob/v3/lib/dialects/abstract/query-generator.js#L2201
// $baseKey = self.quoteIdentifier(key)+'#>>\'{'+path.join(', ')+'}\'';

Remediation

Upgrade sequelize to version 3.35.1 or higher.

References

medium severity

Remote Code Execution (RCE)

  • Vulnerable module: bunyan
  • Introduced through: @aaa-backend-stack/logger@1.16.9 and @aaa-backend-stack/utils@1.16.9

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 bunyan@1.8.5
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 bunyan@1.8.5

Overview

bunyan is an a JSON logging library for node.js services

Affected versions of this package are vulnerable to Remote Code Execution (RCE) via insecure command formatting which allowed creating a "hacked" file in the current dir.

Remediation

Upgrade bunyan to version 1.8.13, 2.0.3 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: glob-parent
  • Introduced through: @aaa-backend-stack/build-tools@1.16.9, @aaa-backend-stack/logger@1.16.9 and others

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 glob-parent@2.0.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 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: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 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: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 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: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 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: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 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: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 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: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 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: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 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: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 npm-watch@0.2.0 nodemon@1.19.4 chokidar@2.1.8 glob-parent@3.1.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 npm-watch@0.2.0 nodemon@1.19.4 chokidar@2.1.8 glob-parent@3.1.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 npm-watch@0.2.0 nodemon@1.19.4 chokidar@2.1.8 glob-parent@3.1.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 npm-watch@0.2.0 nodemon@1.19.4 chokidar@2.1.8 glob-parent@3.1.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 npm-watch@0.2.0 nodemon@1.19.4 chokidar@2.1.8 glob-parent@3.1.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 npm-watch@0.2.0 nodemon@1.19.4 chokidar@2.1.8 glob-parent@3.1.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 npm-watch@0.2.0 nodemon@1.19.4 chokidar@2.1.8 glob-parent@3.1.0
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 npm-watch@0.2.0 nodemon@1.19.4 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

Prototype Pollution

  • Vulnerable module: hoek
  • Introduced through: @aaa-backend-stack/logger@1.16.9 and @aaa-backend-stack/utils@1.16.9

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 joi@6.10.1 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 wreck@6.3.0 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 joi@6.10.1 topo@1.1.0 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 joi@6.10.1 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 wreck@6.3.0 boom@2.10.1 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 wreck@6.3.0 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 joi@6.10.1 topo@1.1.0 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 good@6.6.3 wreck@6.3.0 boom@2.10.1 hoek@2.16.3
    Remediation: Open PR to patch hoek@2.16.3.

Overview

hoek is an Utility methods for the hapi ecosystem.

Affected versions of this package are vulnerable to Prototype Pollution. The utilities function allow modification of the Object prototype. If an attacker can control part of the structure passed to this function, they could add or modify an existing property.

PoC by Olivier Arteau (HoLyVieR)

var Hoek = require('hoek');
var malicious_payload = '{"__proto__":{"oops":"It works !"}}';

var a = {};
console.log("Before : " + a.oops);
Hoek.merge({}, JSON.parse(malicious_payload));
console.log("After : " + a.oops);

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 hoek to version 4.2.1, 5.0.3 or higher.

References

medium severity

Prototype Pollution

  • Vulnerable module: lodash
  • Introduced through: @aaa-backend-stack/build-tools@1.16.9, @aaa-backend-stack/logger@1.16.9 and others

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
    Remediation: Open PR to patch lodash@4.17.15.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

Affected versions of this package are vulnerable to Prototype Pollution. The function zipObjectDeep can be tricked into adding or modifying properties of the Object prototype. These properties will be present on all objects.

PoC

const _ = require('lodash');
_.zipObjectDeep(['__proto__.z'],[123])
console.log(z) // 123

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 lodash to version 4.17.16 or higher.

References

medium severity

Prototype Pollution

  • Vulnerable module: lodash
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

Affected versions of this package are vulnerable to Prototype Pollution. The utilities function allow modification of the Object prototype. If an attacker can control part of the structure passed to this function, they could add or modify an existing property.

PoC by Olivier Arteau (HoLyVieR)

var _= require('lodash');
var malicious_payload = '{"__proto__":{"oops":"It works !"}}';

var a = {};
console.log("Before : " + a.oops);
_.merge({}, JSON.parse(malicious_payload));
console.log("After : " + a.oops);

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 lodash to version 4.17.5 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: lodash
  • Introduced through: @aaa-backend-stack/build-tools@1.16.9, @aaa-backend-stack/logger@1.16.9 and others

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 lodash@4.17.15
  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the toNumber, trim and trimEnd functions.

POC

var lo = require('lodash');

function build_blank (n) {
var ret = "1"
for (var i = 0; i < n; i++) {
ret += " "
}

return ret + "1";
}

var s = build_blank(50000)
var time0 = Date.now();
lo.trim(s)
var time_cost0 = Date.now() - time0;
console.log("time_cost0: " + time_cost0)

var time1 = Date.now();
lo.toNumber(s)
var time_cost1 = Date.now() - time1;
console.log("time_cost1: " + time_cost1)

var time2 = Date.now();
lo.trimEnd(s)
var time_cost2 = Date.now() - time2;
console.log("time_cost2: " + time_cost2)

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 lodash to version 4.17.21 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: lodash
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 lodash@4.12.0
    Remediation: Upgrade to sequelize@3.33.0.

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). It parses dates using regex strings, which may cause a slowdown of 2 seconds per 50k characters.

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 lodash to version 4.17.11 or higher.

References

medium severity
new

HTTP Header Injection

  • Vulnerable module: nodemailer
  • Introduced through: @aaa-backend-stack/logger@1.16.9 and @aaa-backend-stack/utils@1.16.9

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 nodemailer@6.3.1
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 nodemailer@6.3.1

Overview

nodemailer is an Easy as cake e-mail sending from your Node.js applications

Affected versions of this package are vulnerable to HTTP Header Injection if unsanitized user input that may contain newlines and carriage returns is passed into an address object.

PoC:

const userEmail = 'foo@bar.comrnSubject: foobar'; // imagine this comes from e.g. HTTP request params or is otherwise user-controllable
await transporter.sendMail({
from: '...',
to: '...',
replyTo: {
name: 'Customer',
address: userEmail,
},
subject: 'My Subject',
text: message,
});

Remediation

Upgrade nodemailer to version 6.6.1 or higher.

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0
    Remediation: Upgrade to sequelize@4.44.4.

Overview

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

Affected versions of this package are vulnerable to Denial of Service (DoS). The afterResults function for the SQLite dialect fails to catch a TypeError exception for the results variable. This allows attackers to submit malicious input that forces the exception and crashes the Node process.

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 sequelize to version 4.44.4 or higher.

References

medium severity

Prototype Pollution

  • Vulnerable module: undefsafe
  • Introduced through: @aaa-backend-stack/build-tools@1.16.9, @aaa-backend-stack/logger@1.16.9 and others

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 undefsafe@0.0.3
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 undefsafe@0.0.3
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 undefsafe@0.0.3
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 undefsafe@0.0.3
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 undefsafe@0.0.3
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 undefsafe@0.0.3
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 undefsafe@0.0.3
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 undefsafe@0.0.3

Overview

undefsafe is a Simple function for retrieving deep object properties without getting "Cannot read property 'X' of undefined".

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

PoC by JHU System Security Lab

var a = require("undefsafe");
var payload = "__proto__.toString";
a({},payload,"JHU");
console.log({}.toString);

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 undefsafe to version 2.0.3 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 validator@5.7.0
    Remediation: Upgrade to sequelize@6.6.5.

Overview

validator is an A library of string validators and sanitizers.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the isSlug function

PoC

var validator = require("validator")
function build_attack(n) {
    var ret = "111"
    for (var i = 0; i < n; i++) {
        ret += "a"
    }

    return ret+"_";
}
for(var i = 1; i <= 50000; i++) {
    if (i % 10000 == 0) {
        var time = Date.now();
        var attack_str = build_attack(i)
       validator.isSlug(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 validator to version 13.6.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 validator@5.7.0
    Remediation: Upgrade to sequelize@6.6.5.

Overview

validator is an A library of string validators and sanitizers.

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

PoC

var validator = require("validator")
function build_attack(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_attack(i)
       validator.rtrim(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 validator to version 13.6.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 validator@5.7.0
    Remediation: Upgrade to sequelize@6.6.5.

Overview

validator is an A library of string validators and sanitizers.

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

PoC

var validator = require("validator")
function build_attack(n) {
    var ret = "hsla(0"
    for (var i = 0; i < n; i++) {
        ret += " "
    }

    return ret+"◎";
}
for(var i = 1; i <= 50000; i++) {
    if (i % 1000 == 0) {
        var time = Date.now();
        var attack_str = build_attack(i)
       validator.isHSL(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 validator to version 13.6.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 validator@5.7.0
    Remediation: Upgrade to sequelize@6.6.5.

Overview

validator is an A library of string validators and sanitizers.

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

PoC

var validator = require("validator")
function build_attack(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_attack(i)
        validator.isEmail(attack_str,{ allow_display_name: true })
        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 validator to version 13.6.0 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: braces
  • Introduced through: @aaa-backend-stack/build-tools@1.16.9, @aaa-backend-stack/logger@1.16.9 and others

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/git-info@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 @aaa-backend-stack/build-tools@1.16.9 nodemon@1.11.0 chokidar@1.7.0 anymatch@1.3.2 micromatch@2.3.11 braces@1.8.5

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: ms
  • Introduced through: @aaa-backend-stack/logger@1.16.9 and @aaa-backend-stack/utils@1.16.9

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 receptacle@1.3.0 ms@1.0.0
    Remediation: Open PR to patch ms@1.0.0.
  • Introduced through: @aaa-backend-stack/storage@1.16.9 @aaa-backend-stack/utils@1.16.9 @aaa-backend-stack/logger@1.16.9 @aaa-backend-stack/serverdate@1.16.9 receptacle@1.3.0 ms@1.0.0
    Remediation: Open PR to patch ms@1.0.0.

Overview

ms is a tiny millisecond conversion utility.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to an incomplete fix for previously reported vulnerability npm:ms:20151024. The fix limited the length of accepted input string to 10,000 characters, and turned to be insufficient making it possible to block the event loop for 0.3 seconds (on a typical laptop) with a specially crafted string passed to ms() function.

Proof of concept

ms = require('ms');
ms('1'.repeat(9998) + 'Q') // Takes about ~0.3s

Note: Snyk's patch for this vulnerability limits input length to 100 characters. This new limit was deemed to be a breaking change by the author. Based on user feedback, we believe the risk of breakage is very low, while the value to your security is much greater, and therefore opted to still capture this change in a patch for earlier versions as well. Whenever patching security issues, we always suggest to run tests on your code to validate that nothing has been broken.

For more information on Regular Expression Denial of Service (ReDoS) attacks, go to our blog.

Disclosure Timeline

  • Feb 9th, 2017 - Reported the issue to package owner.
  • Feb 11th, 2017 - Issue acknowledged by package owner.
  • April 12th, 2017 - Fix PR opened by Snyk Security Team.
  • May 15th, 2017 - Vulnerability published.
  • May 16th, 2017 - Issue fixed and version 2.0.0 released.
  • May 21th, 2017 - Patches released for versions >=0.7.1, <=1.0.0.

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 ms to version 2.0.0 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: sequelize@3.31.0

Detailed paths

  • Introduced through: @aaa-backend-stack/storage@1.16.9 sequelize@3.31.0 validator@5.7.0
    Remediation: Upgrade to sequelize@4.17.2.

Overview

validator is an A library of string validators and sanitizers.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). It used a regular expression (^\s*data:([a-z]+\/[a-z0-9\-\+]+(;[a-z\-]+=[a-z0-9\-]+)?)?(;base64)?,[a-z0-9!\$&',\(\)\*\+,;=\-\._~:@\/\?%\s]*\s*$) in order to validate Data URIs. This can cause an impact of about 10 seconds matching time for data 70K 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 18th, 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 validator to version 9.4.1 or higher.

References