tool@19.0.0

Vulnerabilities

15 via 95 paths

Dependencies

1021

Source

npm

Find, fix and prevent vulnerabilities in your code.

Severity
  • 7
  • 8
Status
  • 15
  • 0
  • 0

high severity

Remote Memory Exposure

  • Vulnerable module: bl
  • Introduced through: rollup-plugin-node-builtins@2.1.2

Detailed paths

  • Introduced through: tool@19.0.0 rollup-plugin-node-builtins@2.1.2 browserify-fs@1.0.0 levelup@0.18.6 bl@0.8.2

Overview

bl is a library that allows you to collect buffers and access with a standard readable buffer interface.

Affected versions of this package are vulnerable to Remote Memory Exposure. If user input ends up in consume() argument and can become negative, BufferList state can be corrupted, tricking it into exposing uninitialized memory via regular .slice() calls.

PoC by chalker

const { BufferList } = require('bl')
const secret = require('crypto').randomBytes(256)
for (let i = 0; i < 1e6; i++) {
  const clone = Buffer.from(secret)
  const bl = new BufferList()
  bl.append(Buffer.from('a'))
  bl.consume(-1024)
  const buf = bl.slice(1)
  if (buf.indexOf(clone) !== -1) {
    console.error(`Match (at ${i})`, buf)
  }
}

Remediation

Upgrade bl to version 2.2.1, 3.0.1, 4.0.3, 1.2.3 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: engine.io
  • Introduced through: browser-sync@2.26.14

Detailed paths

  • Introduced through: tool@19.0.0 browser-sync@2.26.14 socket.io@2.4.0 engine.io@3.5.0

Overview

engine.io is a realtime engine behind Socket.IO. It provides the foundation of a bidirectional connection between client and server

Affected versions of this package are vulnerable to Denial of Service (DoS) via a POST request to the long polling transport.

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 engine.io to version 4.0.0 or higher.

References

high severity
new

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: normalize-url
  • Introduced through: cssnano@4.1.11

Detailed paths

  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-url@4.0.1 normalize-url@3.3.0
    Remediation: Upgrade to cssnano@5.0.0.

Overview

normalize-url is a Normalize a URL

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

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

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

References

high severity

Arbitrary Code Injection

  • Vulnerable module: serialize-javascript
  • Introduced through: rollup-plugin-terser@3.0.0

Detailed paths

  • Introduced through: tool@19.0.0 rollup-plugin-terser@3.0.0 serialize-javascript@1.9.1
    Remediation: Upgrade to tool@21.0.0.

Overview

serialize-javascript is a package to serialize JavaScript to a superset of JSON that includes regular expressions and functions.

Affected versions of this package are vulnerable to Arbitrary Code Injection. An object like {"foo": /1"/, "bar": "a\"@__R-<UID>-0__@"} would be serialized as {"foo": /1"/, "bar": "a\/1"/}, meaning an attacker could escape out of bar if they controlled both foo and bar and were able to guess the value of <UID>. UID is generated once on startup, is chosen using Math.random() and has a keyspace of roughly 4 billion, so within the realm of an online attack.

PoC

eval('('+ serialize({"foo": /1" + console.log(1)/i, "bar": '"@__R-<UID>-0__@'}) + ')');

Remediation

Upgrade serialize-javascript to version 3.1.0 or higher.

References

high severity

Cross-site Scripting (XSS)

  • Vulnerable module: serialize-javascript
  • Introduced through: rollup-plugin-terser@3.0.0

Detailed paths

  • Introduced through: tool@19.0.0 rollup-plugin-terser@3.0.0 serialize-javascript@1.9.1
    Remediation: Upgrade to tool@21.0.0.

Overview

serialize-javascript is a package to serialize JavaScript to a superset of JSON that includes regular expressions and functions.

Affected versions of this package are vulnerable to Cross-site Scripting (XSS). It does not properly sanitize against unsafe characters in serialized regular expressions. This vulnerability is not affected on Node.js environment since Node.js's implementation of RegExp.prototype.toString() backslash-escapes all forward slashes in regular expressions.

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 serialize-javascript to version 2.1.1 or higher.

References

high severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: trim
  • Introduced through: stylelint@9.10.1

Detailed paths

  • Introduced through: tool@19.0.0 stylelint@9.10.1 postcss-markdown@0.36.0 remark@10.0.1 remark-parse@6.0.3 trim@0.0.1

Overview

trim is a Trim string whitespace

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

PoC by Liyuan Chen:


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

return ret + "1";
}
var time = Date.now();
trim(build_attack(50000))
var time_cost = Date.now() - time;
console.log("time_cost: " + time_cost)```

## 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:
```js
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 trim to version 0.0.3 or higher.

References

high severity
new

Denial of Service (DoS)

  • Vulnerable module: trim-newlines
  • Introduced through: fildes-extra@0.6.0 and stylelint@9.10.1

Detailed paths

  • Introduced through: tool@19.0.0 fildes-extra@0.6.0 cpy@4.0.1 meow@3.7.0 trim-newlines@1.0.0
  • Introduced through: tool@19.0.0 stylelint@9.10.1 meow@5.0.0 trim-newlines@2.0.0
    Remediation: Upgrade to stylelint@13.0.0.

Overview

trim-newlines is a Trim newlines from the start and/or end of a string

Affected versions of this package are vulnerable to Denial of Service (DoS) via the end() method.

Details

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

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

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

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

Two common types of DoS vulnerabilities:

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

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

Remediation

Upgrade trim-newlines to version 3.0.1, 4.0.1 or higher.

References

medium severity

Uninitialized Memory Exposure

  • Vulnerable module: bl
  • Introduced through: rollup-plugin-node-builtins@2.1.2

Detailed paths

  • Introduced through: tool@19.0.0 rollup-plugin-node-builtins@2.1.2 browserify-fs@1.0.0 levelup@0.18.6 bl@0.8.2

Overview

bl is a storage object for collections of Node Buffers.

A possible memory disclosure vulnerability exists when a value of type number is provided to the append() method and results in concatenation of uninitialized memory to the buffer collection.

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');

bl's append function 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.

const BufferList = require('bl')

var bl = new BufferList()
bl.append(new Buffer('abcd'))
bl.append(new Buffer('efg'))
bl.append('100')
// appends a Buffer holding 100 bytes of uninitialized memory
bl.append(100)                     
bl.append(new Buffer('j'))

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

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

Note This is vulnerable only for Node <=4

References

medium severity
new

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: css-what
  • Introduced through: svgo@1.3.2 and cssnano@4.1.11

Detailed paths

  • Introduced through: tool@19.0.0 svgo@1.3.2 css-select@2.1.0 css-what@3.4.2
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-svgo@4.0.3 svgo@1.3.2 css-select@2.1.0 css-what@3.4.2

Overview

css-what is an a CSS selector parser

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via attribute parsing.

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 css-what to version 5.0.1 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: glob-parent
  • Introduced through: stylelint@9.10.1

Detailed paths

  • Introduced through: tool@19.0.0 stylelint@9.10.1 globby@9.2.0 fast-glob@2.2.7 glob-parent@3.1.0
    Remediation: Upgrade to stylelint@13.0.0.

Overview

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

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

PoC by Yeting Li

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

return ret;
}

globParent(build_attack(5000));

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade glob-parent to version 5.1.2 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: postcss
  • Introduced through: postcss@7.0.5

Detailed paths

  • Introduced through: tool@19.0.0 postcss@7.0.5
    Remediation: Upgrade to postcss@7.0.36.

Overview

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) during source map parsing.

PoC

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

// postcss.parse('a{}/*# sourceMappingURL=a.css.map */')
for(var i = 1; i <= 500000; i++) {
    if (i % 10000 == 0) {
        var time = Date.now();
        var attack_str = build_attack(i)
        try{
            postcss.parse(attack_str)
            var time_cost = Date.now() - time;
            console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms");
            }
        catch(e){
        var time_cost = Date.now() - time;
        console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms");
        }
    }
}

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade postcss to version 7.0.36, 8.2.10 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: postcss
  • Introduced through: cssnano@4.1.11, postcss-import@12.0.1 and others

Detailed paths

  • Introduced through: tool@19.0.0 cssnano@4.1.11 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 postcss-import@12.0.1 postcss@7.0.36
    Remediation: Upgrade to postcss-import@13.0.0.
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-reporter@6.0.1 postcss@7.0.36
    Remediation: Upgrade to postcss-reporter@7.0.0.
  • Introduced through: tool@19.0.0 stylelint@9.10.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 autoprefixer@9.8.6 postcss@7.0.36
  • Introduced through: tool@19.0.0 stylelint@9.10.1 autoprefixer@9.8.6 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 css-blank-pseudo@0.1.4 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 css-has-pseudo@0.10.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 css-prefers-color-scheme@3.1.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-attribute-case-insensitive@4.0.2 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-color-functional-notation@2.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-color-gray@5.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-color-hex-alpha@5.0.3 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-color-mod-function@3.0.3 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-color-rebeccapurple@4.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-custom-media@7.0.8 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-custom-properties@8.0.11 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-custom-selectors@5.1.2 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-dir-pseudo-class@5.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-double-position-gradients@1.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-env-function@2.0.2 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-focus-visible@4.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-focus-within@3.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-font-variant@4.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-gap-properties@2.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-image-set-function@3.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-initial@3.0.4 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-lab-function@2.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-logical@3.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-media-minmax@4.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-nesting@7.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-overflow-shorthand@2.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-page-break@2.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-place@4.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-pseudo-class-any-link@6.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-replace-overflow-wrap@3.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-selector-matches@4.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 postcss-preset-env@6.7.0 postcss-selector-not@4.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 stylelint@9.10.1 postcss-reporter@6.0.1 postcss@7.0.36
    Remediation: Upgrade to stylelint@13.7.0.
  • Introduced through: tool@19.0.0 stylelint@9.10.1 postcss-less@3.1.4 postcss@7.0.36
  • Introduced through: tool@19.0.0 stylelint@9.10.1 postcss-safe-parser@4.0.2 postcss@7.0.36
  • Introduced through: tool@19.0.0 stylelint@9.10.1 postcss-sass@0.3.5 postcss@7.0.36
  • Introduced through: tool@19.0.0 stylelint@9.10.1 postcss-scss@2.1.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 stylelint@9.10.1 sugarss@2.0.0 postcss@7.0.36
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 css-declaration-sorter@4.0.1 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 cssnano-util-raw-cache@4.0.1 postcss@7.0.36
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-calc@7.0.5 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-colormin@4.0.3 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-convert-values@4.0.1 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-discard-comments@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-discard-duplicates@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-discard-empty@4.0.1 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-discard-overridden@4.0.1 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-merge-longhand@4.0.11 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-merge-rules@4.0.3 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-minify-font-values@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-minify-gradients@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-minify-params@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-minify-selectors@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-charset@4.0.1 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-display-values@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-positions@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-repeat-style@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-string@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-timing-functions@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-unicode@4.0.1 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-url@4.0.1 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-normalize-whitespace@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-ordered-values@4.1.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-reduce-initial@4.0.3 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-reduce-transforms@4.0.2 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-svgo@4.0.3 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-unique-selectors@4.0.1 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 cssnano@4.1.11 cssnano-preset-default@4.0.8 postcss-merge-longhand@4.0.11 stylehacks@4.0.3 postcss@7.0.36
    Remediation: Upgrade to cssnano@5.0.0.
  • Introduced through: tool@19.0.0 postcss@7.0.5
    Remediation: Upgrade to postcss@8.2.13.
  • Introduced through: tool@19.0.0 postcss-copy@7.1.0 postcss@6.0.23
  • Introduced through: tool@19.0.0 postcss-pxtorem@4.0.1 postcss@5.2.18
    Remediation: Upgrade to postcss-pxtorem@6.0.0.

Overview

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

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

PoC

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

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

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade postcss to version 8.2.13 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: semver
  • Introduced through: rollup-plugin-node-builtins@2.1.2

Detailed paths

  • Introduced through: tool@19.0.0 rollup-plugin-node-builtins@2.1.2 browserify-fs@1.0.0 levelup@0.18.6 semver@2.3.2
    Remediation: Open PR to patch semver@2.3.2.

Overview

semver is a semantic version parser used by npm.

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

Overview

npm is a package manager for javascript.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). The semver module uses regular expressions when parsing a version string. For a carefully crafted input, the time it takes to process these regular expressions is not linear to the length of the input. Since the semver module did not enforce a limit on the version string length, an attacker could provide a long string that would take up a large amount of resources, potentially taking a server down. This issue therefore enables a potential Denial of Service attack. This is a slightly differnt variant of a typical Regular Expression Denial of Service (ReDoS) vulnerability.

Details

<>

Remediation

Update to a version 4.3.2 or greater. From the issue description [2]: "Package version can no longer be more than 256 characters long. This prevents a situation in which parsing the version number can use exponentially more time and memory to parse, leading to a potential denial of service."

References

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 semver to version 4.3.2 or higher.

References

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: stylelint
  • Introduced through: stylelint@9.10.1

Detailed paths

  • Introduced through: tool@19.0.0 stylelint@9.10.1
    Remediation: Upgrade to tool@21.0.0.

Overview

stylelint is a linter that helps you avoid errors and enforce conventions in your styles.

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

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 stylelint to version 11.0.0 or higher.

References

medium severity

Prototype Pollution

  • Vulnerable module: yargs-parser
  • Introduced through: stylelint@9.10.1

Detailed paths

  • Introduced through: tool@19.0.0 stylelint@9.10.1 meow@5.0.0 yargs-parser@10.1.0
    Remediation: Upgrade to stylelint@13.0.0.

Overview

yargs-parser is a mighty option parser used by yargs.

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 __proto__ payload.

Our research team checked several attack vectors to verify this vulnerability:

  1. It could be used for privilege escalation.
  2. The library could be used to parse user input received from different sources:
    • terminal emulators
    • system calls from other code bases
    • CLI RPC servers

PoC by Snyk

const parser = require("yargs-parser");
console.log(parser('--foo.__proto__.bar baz'));
console.log(({}).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

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 yargs-parser to version 5.0.1, 13.1.2, 15.0.1, 18.1.1 or higher.

References