sequelize@1.6.0

Vulnerabilities

17 via 17 paths

Dependencies

8

Source

npm

Find, fix and prevent vulnerabilities in your code.

Severity
  • 5
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  • 1
Status
  • 17
  • 0
  • 0

high severity

Hash Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@4.12.0.

Overview

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

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

For example:

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

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

Remediation

Upgrade sequelize to version 4.12.0 or higher.

References

high severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@3.35.1.

Overview

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

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

PoC by Snyk

const Sequelize = require('sequelize');

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

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

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

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

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

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

Remediation

Upgrade sequelize to version 3.35.1 or higher.

References

high severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@2.0.0.

Overview

sequelize versions prior to 2.0.0-rc7 are vulnerable to SQL Injection attacks if untrusted user input is passed into the order parameter.

Remediation

Upgrade to version 2.0.0-rc8 or greater.

References

high severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@3.14.0.

Overview

sequelize versions prior to 3.17.0 are vulnerable to SQL Injection attacks if untrusted user input is passed into the order or limit parameters.

Example

models.User.findAll({
  limit: '1; DELETE FROM "Users" WHERE 1=1; --',
}).then(function (users) {
  console.log(users);
});

Remediation

Upgrade to version 3.17.0 or greater.

References

high severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: validator
  • Introduced through: validator@0.4.28

Detailed paths

  • Introduced through: sequelize@1.6.0 validator@0.4.28
    Remediation: Upgrade to sequelize@2.0.0.

Overview

validator is a library of string validators and sanitizers.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) attacks. It used a regular expression in order to validate URLs.

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Update to version 3.22.1 or greater.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: moment
  • Introduced through: moment@1.7.2

Detailed paths

  • Introduced through: sequelize@1.6.0 moment@1.7.2
    Remediation: Upgrade to sequelize@2.0.0.

Overview

moment is a lightweight JavaScript date library for parsing, validating, manipulating, and formatting dates.

An attacker can provide a long value to the duration function, which nearly matches the pattern being matched. This will cause the regular expression matching to take a long time, all the while occupying the event loop and preventing it from processing other requests and making the server unavailable (a Denial of Service attack).

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 moment to version 2.11.2 or greater.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: moment
  • Introduced through: moment@1.7.2

Detailed paths

  • Introduced through: sequelize@1.6.0 moment@1.7.2
    Remediation: Upgrade to sequelize@2.0.0.

Overview

moment is a lightweight JavaScript date library for parsing, validating, manipulating, and formatting dates.

Affected versions of the package are vulnerable to Regular Expression Denial of Service (ReDoS) attacks for any locale that has separate format and standalone options and format input can be controlled by the user.

An attacker can provide a specially crafted input to the format function, which nearly matches the pattern being matched. This will cause the regular expression matching to take a long time, all the while occupying the event loop and preventing it from processing other requests and making the server unavailable (a Denial of Service attack).

Disclosure Timeline

  • October 19th, 2016 - Reported the issue to package owner.
  • October 19th, 2016 - Issue acknowledged by package owner.
  • October 24th, 2016 - Issue fixed and version 2.15.2 released.

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.

References

medium severity

Denial of Service (DoS)

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@4.44.4.

Overview

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

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

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade sequelize to version 4.44.4 or higher.

References

medium severity

Remote Memory Exposure

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@3.17.2.

Overview

A potential memory disclosure vulnerability exists in sequelize versions prior to 3.17.2. A field of type DataTypes.BLOB can be used to expose sensitive information such as code, runtime memory and user data into the database.

Details

sequelize uses the Buffer type to represent DataTypes.BLOB. Initializing a Buffer with integer N creates a Buffer of length N with non zero-ed out memory. Example:

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

Initializing a BLOB field in such manner will dump uninitialized memory into the database. The patch wraps Buffer field initialization in sequelize by converting a number value N to a string, initializing the Buffer with N in its ascii form.

Proof of concept

var Sequelize = require('sequelize');
var sequelize = new Sequelize('pastebin', null, null,
    { host: '127.0.0.1', dialect: 'postgres', });

var Task = sequelize.define('Pastebin', {
    title: Sequelize.STRING,
    content: Sequelize.BLOB,
  });

Task.create({
  title: 'title',
  content: 100,
}).then(function (task) {
  console.log(task.title);
  console.log(task.content); // will print out 100 bytes of previously used memory
});

Remediation

Upgrade sequelize to version >= 3.17.3

References

medium severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@1.7.0.

Overview

sequelize versions prior to 1.7.0-alpha3 are vulnerable to SQL Injection attacks if untrusted user input is passed into the order parameter.

Remediation

Upgrade to version 1.7.0-alpha3 or greater.

References

medium severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@3.0.0.

Overview

Beginning with sequelize version 3.0.0, two security related changes were introduced:

  • findOne no longer takes a string / integer / binary argument to represent a primaryKey. Use findById instead.
  • where: "raw query" is no longer legal, you must now explicitly use where: ["raw query", [replacements]]

Remediation

Upgrade to version 3.0.0 or greater.

References

medium severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@3.12.1.

Overview

sequelize is a multi dialect ORM for Node.JS/io.js. Affected versions of the package are vulnerable to SQL Injection via the user search by typing the name they want to search for and the application then executes:

User.findAll( { where: { name: req.body.name } } )

a malicious attacker may exploit this to find the users password by entering $password$. This could be avoided by specifying the exact column to search: the user column.

Remediation

Upgrade sequelize to version 3.12.1 or higher.

References

medium severity

SQL Injection

  • Vulnerable module: sequelize
  • Introduced through: sequelize@1.6.0

Detailed paths

  • Introduced through: sequelize@1.6.0
    Remediation: Upgrade to sequelize@3.20.0.

Overview

sequelize versions prior to 3.20.0 improperly escape arrays of strings bound to named parameters.

Remediation

Upgrade to version 3.20.0 or greater.

References

medium severity

Buffer Overflow

  • Vulnerable module: validator
  • Introduced through: validator@0.4.28

Detailed paths

  • Introduced through: sequelize@1.6.0 validator@0.4.28
    Remediation: Upgrade to sequelize@3.23.2.

Overview

validator is a library of string validators and sanitizers.

Affected versions of this package are vulnerable to Buffer Overflow. It used a regular expression (/^(?:[A-Z0-9+\/]{4})*(?:[A-Z0-9+\/]{2}==|[A-Z0-9+\/]{3}=|[A-Z0-9+\/]{4})$/i) in order to validate Base64 strings.

Remediation

Upgrade validator to version 5.0.0 or higher.

References

medium severity

Cross-site Scripting (XSS)

  • Vulnerable module: validator
  • Introduced through: validator@0.4.28

Detailed paths

  • Introduced through: sequelize@1.6.0 validator@0.4.28
    Remediation: Upgrade to sequelize@1.7.0.

Overview

validator is a module for Node.js contains functionality meant to filter potential XSS attacks (a filter called xss). A method of bypassing the filter via an encoded URL has been publicly disclosed. In general, because the function’s filtering is blacklist-based it is likely that other bypasses will be discovered in the future. Developers are encouraged not to use the xss filter function in this package. The xss() function removes the word "javascript" when contained inside an attribute. However, it does not properly handle cases where characters have been hex-encoded. As a result, it is possible to build an input that bypasses the filter but which the browser will accept as valid JavaScript.

For example, browsers interpret abc as abc.

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 the latest version of this library. However, it should be noted that the fix for this vulnerability was to remove the xss filter functionality. Seek another library to provide proper output encoding.

References

-GitHub Issue

medium severity

Cross-site Scripting (XSS)

  • Vulnerable module: validator
  • Introduced through: validator@0.4.28

Detailed paths

  • Introduced through: sequelize@1.6.0 validator@0.4.28
    Remediation: Upgrade to sequelize@1.7.0.

Overview

The validator module for Node.js contains functionality meant to filter potential XSS attacks (a filter called xss). Several ways to bypass the filter were discovered. In general, because the function’s filtering is blacklist-based it is likely that other bypasses will be discovered in the future. Developers are encouraged not to use the xss filter function in this package.

Source: Node Security Project

Details

Various inputs that could bypass the filter were discovered:

Improper parsing of nested tags:

<s <onmouseover="alert(1)"> <;s onmouseover="alert(1)">This is a test</s>

Incomplete filtering of javascript: URIs:

<a href="javascriptJ a V a S c R iPt::alert(1)" "<s>">test</a>

UI Redressing:

<div style="z-index: 9999999; background-color: green; width: 100%; height: 100%">
<h1>You have won</h1>Please click the link and enter your login details:
<a href="http://example.com/">http://good.com</a>
</div>

Bypass via Nested Forbidden Strings:

<scrRedirecRedirect 302t 302ipt type="text/javascript">prompt(1);</scrRedirecRedirect 302t 302ipt>

Additional bypasses were discovered by Krzysztof Kotowicz in 2012 when auditing CodeIgniter's XSS filtering function, which this code was based off of.

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

If you are a developer currently using the xss filter function from the validator package, you should consider replacing it with the escape filter function from the same package. This function replaces all instances of angle brackets (<, >), ampersands, and quotation marks, so no HTML tags will be processed.

References

low severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: moment
  • Introduced through: moment@1.7.2

Detailed paths

  • Introduced through: sequelize@1.6.0 moment@1.7.2
    Remediation: Upgrade to sequelize@2.0.0.

Overview

moment is a lightweight JavaScript date library for parsing, validating, manipulating, and formatting dates.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). It used a regular expression (/[0-9]*['a-z\u00A0-\u05FF\u0700-\uD7FF\uF900-\uFDCF\uFDF0-\uFFEF]+|[\u0600-\u06FF\/]+(\s*?[\u0600-\u06FF]+){1,2}/i) in order to parse dates specified as strings. This can cause a very low impact of about 2 seconds matching time for data 50k characters long.

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade moment to version 2.19.3 or higher.

References