Find, fix and prevent vulnerabilities in your code.

Overview

morgan is a HTTP request logger middleware for node.js.

Affected versions of this package are vulnerable to Arbitrary Code Injection. An attacker could use the format parameter to inject arbitrary commands.

Remediation

Upgrade morgan to version 1.9.1 or higher.

References

• GitHub Commit
• HackerOne Report

Overview

helmet-csp is a Content Security Policy that helps prevent unwanted content being injected into your webpages.

Affected versions of this package are vulnerable to Configuration Override affecting the application's Content Security Policy (CSP). It's browser sniffing for Firefox deletes the default-src CSP policy, which is the fallback policy. This allows an attacker to remove an application's default CSP.

Remediation

Upgrade helmet-csp to version 2.9.2 or higher.

References

• GitHub Commit
• NPM Security Advisory

Overview

request is a simplified http request client.

Affected versions of this package are vulnerable to Server-side Request Forgery (SSRF) due to insufficient checks in the lib/redirect.js file by allowing insecure redirects in the default configuration, via an attacker-controller server that does a cross-protocol redirect (HTTP to HTTPS, or HTTPS to HTTP).

NOTE: request package has been deprecated, so a fix is not expected. See https://github.com/request/request/issues/3142.

Remediation

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

References

• GitHub Commit
• GitHub Issue
• GitHub PR

Overview

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

Affected versions of this package are vulnerable to Uncontrolled Resource Consumption ('Resource Exhaustion') due to the lack of folders count validation during the folder creation process. An attacker who generates a large number of sub-folders can consume memory on the system running the software and even crash the client within few seconds of running it using a path with too many sub-folders inside.

Remediation

Upgrade tar to version 6.2.1 or higher.

References

• GitHub Commit

Overview

tough-cookie is a RFC6265 Cookies and CookieJar module for Node.js.

Affected versions of this package are vulnerable to Prototype Pollution due to improper handling of Cookies when using CookieJar in rejectPublicSuffixes=false mode. Due to an issue with the manner in which the objects are initialized, an attacker can expose or modify a limited amount of property information on those objects. There is no impact to availability.

PoC

// PoC.js
async function main(){
var tough = require("tough-cookie");
var cookiejar = new tough.CookieJar(undefined,{rejectPublicSuffixes:false});
// Exploit cookie
await cookiejar.setCookie(
  "Slonser=polluted; Domain=__proto__; Path=/notauth",
  "https://__proto__/admin"
);
// normal cookie
var cookie = await cookiejar.setCookie(
  "Auth=Lol; Domain=google.com; Path=/notauth",
  "https://google.com/"
);

//Exploit cookie
var a = {};
console.log(a["/notauth"]["Slonser"])
}
main();

Details

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

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

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

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

merge (target, source)

  foreach property of source

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

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

    else

      target[property] = source[property]

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

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

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

Property definition by path

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

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

Types of attacks

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

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

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

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

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

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

For more information on this vulnerability type:

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

Remediation

Upgrade tough-cookie to version 4.1.3 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub Release

Overview

xmldom is an A pure JavaScript W3C standard-based (XML DOM Level 2 Core) DOMParser and XMLSerializer module.

Affected versions of this package are vulnerable to Improper Input Validation. It does not correctly escape special characters when serializing elements are removed from their ancestor. This may lead to unexpected syntactic changes during XML processing in some downstream applications.

Note: Customers who use "xmldom" package, should use "@xmldom/xmldom" instead, as "xmldom" is no longer maintained.

Remediation

There is no fixed version for xmldom.

References

• GitHub Commit
• GitHub Issue

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.

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

• GitHub Commit
• GitHub Commit
• GitHub Issue
• GitHub PR
• HackerOne Report
• NPM Security Advisory

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

• GitHub Commit
• GitHub Issue

Overview

Affected versions of this package are vulnerable to Missing Release of Resource after Effective Lifetime via the makeres function due to improperly deleting keys from the reqs object after execution of callbacks. This behavior causes the keys to remain in the reqs object, which leads to resource exhaustion.

Exploiting this vulnerability results in crashing the node process or in the application crash.

Note: This library is not maintained, and currently, there is no fix for this issue. To overcome this vulnerability, several dependent packages have eliminated the use of this library.

To trigger the memory leak, an attacker would need to have the ability to execute or influence the asynchronous operations that use the inflight module within the application. This typically requires access to the internal workings of the server or application, which is not commonly exposed to remote users. Therefore, “Attack vector” is marked as “Local”.

PoC

const inflight = require('inflight');

function testInflight() {
  let i = 0;
  function scheduleNext() {
    let key = `key-${i++}`;
    const callback = () => {
    };
    for (let j = 0; j < 1000000; j++) {
      inflight(key, callback);
    }

    setImmediate(scheduleNext);
  }


  if (i % 100 === 0) {
    console.log(process.memoryUsage());
  }

  scheduleNext();
}

testInflight();

Remediation

There is no fixed version for inflight.

References

• GitHub Issue
• GitHub PR

Overview

https-proxy-agent is a module that provides an http.Agent implementation that connects to a specified HTTP or HTTPS proxy server, and can be used with the built-in https module.

Affected versions of this package are vulnerable to Man-in-the-Middle (MitM). When targeting a HTTP proxy, https-proxy-agent opens a socket to the proxy, and sends the proxy server a CONNECT request. If the proxy server responds with something other than a HTTP response 200, https-proxy-agent incorrectly returns the socket without any TLS upgrade. This request data may contain basic auth credentials or other secrets, is sent over an unencrypted connection. A suitably positioned attacker could steal these secrets and impersonate the client.

PoC by Kris Adler

var url = require('url');
var https = require('https');
var HttpsProxyAgent = require('https-proxy-agent');

var proxyOpts = url.parse('http://127.0.0.1:80');
var opts = url.parse('https://www.google.com');
var agent = new HttpsProxyAgent(proxyOpts);
opts.agent = agent;
opts.auth = 'username:password';
https.get(opts);

Remediation

Upgrade https-proxy-agent to version 2.2.3 or higher.

References

• GitHub Commit
• GitHub PR
• HackerOne Report

Overview

minimist is a parse argument options module.

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

PoC by Snyk

require('minimist')('--__proto__.injected0 value0'.split(' '));
console.log(({}).injected0 === 'value0'); // true

require('minimist')('--constructor.prototype.injected1 value1'.split(' '));
console.log(({}).injected1 === 'value1'); // true

Details

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

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

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

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

merge (target, source)

  foreach property of source

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

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

    else

      target[property] = source[property]

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

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

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

Property definition by path

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

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

Types of attacks

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

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

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

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

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

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

For more information on this vulnerability type:

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

Remediation

Upgrade minimist to version 0.2.1, 1.2.3 or higher.

References

• Command Injection PoC
• GitHub Fix Commit #1
• GitHub Fix Commit #2
• Snyk Research Blog

Overview

mongoose is a Mongoose is a MongoDB object modeling tool designed to work in an asynchronous environment.

Affected versions of this package are vulnerable to Prototype Pollution. The mongoose.Schema() function is subject to prototype pollution due to the recursively calling of Schema.prototype.add() function to add new items into the schema object. This vulnerability allows modification of the Object prototype.

PoC

mongoose = require('mongoose');
mongoose.version; //'5.12.0'
var malicious_payload = '{"__proto__":{"polluted":"HACKED"}}';
console.log('Before:', {}.polluted); // undefined
mongoose.Schema(JSON.parse(malicious_payload));
console.log('After:', {}.polluted); // HACKED

Details

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

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

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

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

merge (target, source)

  foreach property of source

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

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

    else

      target[property] = source[property]

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

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

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

Property definition by path

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

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

Types of attacks

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

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

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

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

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

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

For more information on this vulnerability type:

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

Remediation

Upgrade mongoose to version 5.12.2 or higher.

References

• GitHub Commit
• GitHub Issue

Overview

mpath is a package that gets/sets javascript object values using MongoDB-like path notation.

Affected versions of this package are vulnerable to Prototype Pollution. A type confusion vulnerability can lead to a bypass of CVE-2018-16490. In particular, the condition ignoreProperties.indexOf(parts[i]) !== -1 returns -1 if parts[i] is ['__proto__']. This is because the method that has been called if the input is an array is Array.prototype.indexOf() and not String.prototype.indexOf(). They behave differently depending on the type of the input.

PoC

const mpath = require('mpath');
// mpath.set(['__proto__', 'polluted'], 'yes', {});
// console.log(polluted); // ReferenceError: polluted is not defined

mpath.set([['__proto__'], 'polluted'], 'yes', {});
console.log(polluted); // yes

Details

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

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

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

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

merge (target, source)

  foreach property of source

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

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

    else

      target[property] = source[property]

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

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

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

Property definition by path

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

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

Types of attacks

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

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

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

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

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

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

For more information on this vulnerability type:

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

Remediation

Upgrade mpath to version 0.8.4 or higher.

References

• GitHub Commit
• Snyk Blog

Overview

underscore is a JavaScript's functional programming helper library.

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

PoC

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

Remediation

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

References

• GitHub Additional Information
• GitHub Commit

Overview

xmldom is an A pure JavaScript W3C standard-based (XML DOM Level 2 Core) DOMParser and XMLSerializer module.

Affected versions of this package are vulnerable to XML External Entity (XXE) Injection. Does not correctly preserve system identifiers, FPIs or namespaces when repeatedly parsing and serializing maliciously crafted documents.

Details

XXE Injection is a type of attack against an application that parses XML input. XML is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. By default, many XML processors allow specification of an external entity, a URI that is dereferenced and evaluated during XML processing. When an XML document is being parsed, the parser can make a request and include the content at the specified URI inside of the XML document.

Attacks can include disclosing local files, which may contain sensitive data such as passwords or private user data, using file: schemes or relative paths in the system identifier.

For example, below is a sample XML document, containing an XML element- username.

<xml>
<?xml version="1.0" encoding="ISO-8859-1"?>
   <username>John</username>
</xml>

An external XML entity - xxe, is defined using a system identifier and present within a DOCTYPE header. These entities can access local or remote content. For example the below code contains an external XML entity that would fetch the content of /etc/passwd and display it to the user rendered by username.

<xml>
<?xml version="1.0" encoding="ISO-8859-1"?>
<!DOCTYPE foo [
   <!ENTITY xxe SYSTEM "file:///etc/passwd" >]>
   <username>&xxe;</username>
</xml>

Other XXE Injection attacks can access local resources that may not stop returning data, possibly impacting application availability and leading to Denial of Service.

Remediation

Upgrade xmldom to version 0.5.0 or higher.

References

• GitHub Commit
• RedHat Bugzilla Bug

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) by providing a custom locale rule that makes it possible to assign the parameter in posPre: ' '.repeat() of NUMBER_FORMATS.PATTERNS[1].posPre with a very high value.

Note:

1. This package has been deprecated and is no longer maintained.

2. The vulnerable versions are 1.7.0 and higher.

PoC:

class AppCtrl {
  constructor($locale, $timeout) {
    'ngInject';
    const ctrl = this;
    ctrl.currencySymbol = '$';
    ctrl.amount = 100;
    ctrl.posPre = $locale.NUMBER_FORMATS.PATTERNS[1].posPre;

    ctrl.onPosPreChange = () => {
      $locale.NUMBER_FORMATS.PATTERNS[1].posPre = ctrl.posPre;
      const amount = ctrl.amount;
      ctrl.amount = 0;
      $timeout(() => (ctrl.amount = amount));
    };

    ctrl.onReDos = () => {
      ctrl.currencySymbol = '';
      ctrl.posPre = ' '.repeat(1000000);
      $locale.NUMBER_FORMATS.PATTERNS[1].posPre = ctrl.posPre;
    };
  }
}

export default AppCtrl;

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

There is no fixed version for angular.

References

• PoC

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the angular.copy() utility function due to the usage of an insecure regular expression. Exploiting this vulnerability is possible by a large carefully-crafted input, which can result in catastrophic backtracking.

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

There is no fixed version for angular.

References

• PoC

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the $resource service due to the usage of an insecure regular expression. Exploiting this vulnerability is possible by a large carefully-crafted input, which can result in catastrophic backtracking.

PoC

The vulnerability manifests itself when the $resource service is used with a URL that contains a large number of slashes followed by a non-slash character (for example, /some/url/////.../////foo):

$resource('/some/url/${manySlashesFollowedByNonSlash}`).query();

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

There is no fixed version for angular.

References

• PoC

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the <input type="url"> element due to the usage of an insecure regular expression in the input[url] functionality. Exploiting this vulnerability is possible by a large carefully-crafted input, which can result in catastrophic backtracking.

PoC

The vulnerability manifests itself when a <input type="url"> element is filled with an invalid URL consisting of any scheme followed by a large number of slashes (for example, http://///.../////):

<input type="url" ng-model="urlWithManySlashes" />

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

There is no fixed version for angular.

References

• PoC

Overview

hosted-git-info is a Provides metadata and conversions from repository urls for Github, Bitbucket and Gitlab

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

PoC by Yeting Li

var hostedGitInfo = require("hosted-git-info")
function build_attack(n) {
    var ret = "a:"
    for (var i = 0; i < n; i++) {
        ret += "a"
    }
    return ret + "!";
}

for(var i = 1; i <= 5000000; i++) {
   if (i % 1000 == 0) {
        var time = Date.now();
        var attack_str = build_attack(i)
       var parsedInfo = hostedGitInfo.fromUrl(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.

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 hosted-git-info to version 3.0.8, 2.8.9 or higher.

References

• GitHub Commit

Overview

minimatch is a minimal matching utility.

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

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade minimatch to version 3.0.5 or higher.

References

• GitHub Commit

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the attachDataUrls parameter or when parsing attachments with an embedded file. An attacker can exploit this vulnerability by sending a specially crafted email that triggers inefficient regular expression evaluation, leading to excessive consumption of CPU resources.

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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 nodemailer to version 6.9.9 or higher.

References

• GitHub Commit
• GitHub Gist
• GitHub Gist

Overview

npm is a package manager for JavaScript.

Affected versions of this package are vulnerable to Access Restriction Bypass. It might allow local users to bypass intended filesystem access restrictions due to ownerships of /etc and /usr directories are being changed unexpectedly, related to a "correctMkdir" issue.

Remediation

Upgrade npm to version 5.7.1 or higher.

References

• GitHub Commit
• GitHub Issue

Overview

npm is a package manager for JavaScript.

Affected versions of this package are vulnerable to Insertion of Sensitive Information into Log File. The CLI supports URLs like <protocol>://[<user>[:<password>]@]<hostname>[:<port>][:][/]<path>. The password value is not redacted and is printed to stdout and also to any generated log files.

Remediation

Upgrade npm to version 6.14.6 or higher.

References

• GitHub Commit

Overview

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

Affected versions of this package are vulnerable to Improper Input Validation when parsing external Cascading Style Sheets (CSS) with linters using PostCSS. An attacker can cause discrepancies by injecting malicious CSS rules, such as @font-face{ font:(\r/*);}. This vulnerability is because of an insecure regular expression usage in the RE_BAD_BRACKET variable.

Remediation

Upgrade postcss to version 8.4.31 or higher.

References

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

Overview

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

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

PoC

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

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

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade postcss to version 8.2.13, 7.0.36 or higher.

References

• GitHub Commit

Overview

uglify-js is a JavaScript parser, minifier, compressor and beautifier toolkit.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the string_template and the decode_template functions.

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade uglify-js to version 3.14.3 or higher.

References

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

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). A specially crafted value of the Sec-Websocket-Protocol header can be used to significantly slow down a ws server.

##PoC

for (const length of [1000, 2000, 4000, 8000, 16000, 32000]) {
  const value = 'b' + ' '.repeat(length) + 'x';
  const start = process.hrtime.bigint();

  value.trim().split(/ *, */);

  const end = process.hrtime.bigint();

  console.log('length = %d, time = %f ns', length, end - start);
}

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade ws to version 7.4.6, 6.2.2, 5.2.3 or higher.

References

• GitHub Commit

Overview

Affected versions of this package are vulnerable to Prototype Pollution due to allowing an external attacker to edit or add new properties to an object. This is possible because the application does not properly validate incoming JSON keys, thus allowing the __proto__ property to be edited.

PoC

var parseString = require('xml2js').parseString;

let normal_user_request    = "<role>admin</role>";
let malicious_user_request = "<__proto__><role>admin</role></__proto__>";

const update_user = (userProp) => {
    // A user cannot alter his role. This way we prevent privilege escalations.
    parseString(userProp, function (err, user) {
        if(user.hasOwnProperty("role") && user?.role.toLowerCase() === "admin") {
            console.log("Unauthorized Action");
        } else {
            console.log(user?.role[0]);
        }
    });
}

update_user(normal_user_request);
update_user(malicious_user_request);

Details

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

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

• Unsafe Object recursive merge

• Property definition by path

Unsafe Object recursive merge

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

merge (target, source)

  foreach property of source

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

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

    else

      target[property] = source[property]

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

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

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

Property definition by path

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

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

Types of attacks

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

Affected environments

The following environments are susceptible to a Prototype Pollution attack:

• Application server

• Web server

• Web browser

How to prevent

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

2. Require schema validation of JSON input.

3. Avoid using unsafe recursive merge functions.

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

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

For more information on this vulnerability type:

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

Remediation

Upgrade xml2js to version 0.5.0 or higher.

References

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

Overview

tunnel-agent is HTTP proxy tunneling agent. Affected versions of the package are vulnerable to Uninitialized Memory Exposure.

A possible memory disclosure vulnerability exists when a value of type number is used to set the proxy.auth option of a request request and results in a possible uninitialized memory exposures in the request body.

This is a result of unobstructed use of the Buffer constructor, whose insecure default constructor increases the odds of memory leakage.

Details

Constructing a Buffer class with integer N creates a Buffer of length N with raw (not "zero-ed") memory.

In the following example, the first call would allocate 100 bytes of memory, while the second example will allocate the memory needed for the string "100":

// uninitialized Buffer of length 100
x = new Buffer(100);
// initialized Buffer with value of '100'
x = new Buffer('100');

tunnel-agent's request construction uses the default Buffer constructor as-is, making it easy to append uninitialized memory to an existing list. If the value of the buffer list is exposed to users, it may expose raw server side memory, potentially holding secrets, private data and code. This is a similar vulnerability to the infamous Heartbleed flaw in OpenSSL.

Proof of concept by ChALkeR

require('request')({
  method: 'GET',
  uri: 'http://www.example.com',
  tunnel: true,
  proxy:{
      protocol: 'http:',
      host:"127.0.0.1",
      port:8080,
      auth:80
  }
});

You can read more about the insecure Buffer behavior on our blog.

Similar vulnerabilities were discovered in request, mongoose, ws and sequelize.

Remediation

Upgrade tunnel-agent to version 0.6.0 or higher. Note This is vulnerable only for Node <=4

References

• GitHub Commit
• PoC by ChALkeR

Overview

chownr is a package that takes the same arguments as fs.chown()

Affected versions of this package are vulnerable to Time of Check Time of Use (TOCTOU). Affected versions of this package are vulnerable toTime of Check Time of Use (TOCTOU) attacks.

It does not dereference symbolic links and changes the owner of the link, which can trick it into descending into unintended trees if a non-symlink is replaced by a symlink at a critical moment:

      fs.lstat(pathChild, function(er, stats) {
        if (er)
          return cb(er)
        if (!stats.isSymbolicLink())
          chownr(pathChild, uid, gid, then)

Remediation

Upgrade chownr to version 1.1.0 or higher.

References

• GitHub Issue

Overview

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

Affected versions of this package are vulnerable to Cross-site Scripting (XSS) due to insecure page caching in the Internet Explorer browser, which allows interpolation of <textarea> elements.

Details

A cross-site scripting attack occurs when the attacker tricks a legitimate web-based application or site to accept a request as originating from a trusted source.

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

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

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

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

Types of attacks

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

Affected environments

The following environments are susceptible to an XSS attack:

• Web servers
• Application servers
• Web application environments

How to prevent

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

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

Remediation

There is no fixed version for angular.

References

• PoC