Vulnerabilities

 5 via 8 paths

Dependencies

 82

Source

 GitHub

Commit

 868dbefa

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critical severity

Heap-based Buffer Overflow

  • Vulnerable module: sharp
  • Introduced through: sharp@0.27.2

Detailed paths

  • Introduced through: moleculer-sharp@designtesbrot/moleculer-sharp#868dbefa0a9b579ae2acaf0cd571aa6e48dc3368 › sharp@0.27.2
    Remediation: Upgrade to sharp@0.32.6.

Overview

sharp is a High performance Node.js image processing, the fastest module to resize JPEG, PNG, WebP, GIF, AVIF and TIFF images

Affected versions of this package are vulnerable to Heap-based Buffer Overflow when the ReadHuffmanCodes() function is used. An attacker can craft a special WebP lossless file that triggers the ReadHuffmanCodes() function to allocate the HuffmanCode buffer with a size that comes from an array of precomputed sizes: kTableSize. The color_cache_bits value defines which size to use. The kTableSize array only takes into account sizes for 8-bit first-level table lookups but not second-level table lookups. libwebp allows codes that are up to 15-bit (MAX_ALLOWED_CODE_LENGTH). When BuildHuffmanTable() attempts to fill the second-level tables it may write data out-of-bounds. The OOB write to the undersized array happens in ReplicateValue.

Notes:

This is only exploitable if the color_cache_bits value defines which size to use.

This vulnerability was also published on libwebp CVE-2023-5129

Changelog:

2023-09-12: Initial advisory publication

2023-09-27: Advisory details updated, including CVSS, references

2023-09-27: CVE-2023-5129 rejected as a duplicate of CVE-2023-4863

2023-09-28: Research and addition of additional affected libraries

2024-01-28: Additional fix information

Remediation

Upgrade sharp to version 0.32.6 or higher.

References

Heap-based Buffer Overflow vulnerability report

high severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ansi-regex
  • Introduced through: sharp@0.27.2

Detailed paths

  • Introduced through: moleculer-sharp@designtesbrot/moleculer-sharp#868dbefa0a9b579ae2acaf0cd571aa6e48dc3368 › sharp@0.27.2 › npmlog@4.1.2 › gauge@2.7.4 › strip-ansi@3.0.1 › ansi-regex@2.1.1
    Remediation: Upgrade to sharp@0.28.0.
  • Introduced through: moleculer-sharp@designtesbrot/moleculer-sharp#868dbefa0a9b579ae2acaf0cd571aa6e48dc3368 › sharp@0.27.2 › npmlog@4.1.2 › gauge@2.7.4 › string-width@1.0.2 › strip-ansi@3.0.1 › ansi-regex@2.1.1
    Remediation: Upgrade to sharp@0.28.0.
  • Introduced through: moleculer-sharp@designtesbrot/moleculer-sharp#868dbefa0a9b579ae2acaf0cd571aa6e48dc3368 › sharp@0.27.2 › prebuild-install@6.1.4 › npmlog@4.1.2 › gauge@2.7.4 › strip-ansi@3.0.1 › ansi-regex@2.1.1
    Remediation: Upgrade to sharp@0.29.3.
  • Introduced through: moleculer-sharp@designtesbrot/moleculer-sharp#868dbefa0a9b579ae2acaf0cd571aa6e48dc3368 › sharp@0.27.2 › prebuild-install@6.1.4 › npmlog@4.1.2 › gauge@2.7.4 › string-width@1.0.2 › strip-ansi@3.0.1 › ansi-regex@2.1.1
    Remediation: Upgrade to sharp@0.29.3.

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to the sub-patterns [[\\]()#;?]* and (?:;[-a-zA-Z\\d\\/#&.:=?%@~_]*)*.

PoC

import ansiRegex from 'ansi-regex';

for(var i = 1; i <= 50000; i++) {
    var time = Date.now();
    var attack_str = "\u001B["+";".repeat(i*10000);
    ansiRegex().test(attack_str)
    var time_cost = Date.now() - time;
    console.log("attack_str.length: " + attack_str.length + ": " + time_cost+" ms")
}

Details

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

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

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

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

This regular expression accomplishes the following:

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

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

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

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

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

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

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

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

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

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

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

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

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

Remediation

Upgrade ansi-regex to version 3.0.1, 4.1.1, 5.0.1, 6.0.1 or higher.

References

Regular Expression Denial of Service (ReDoS) vulnerability report

medium severity

Information Exposure

  • Vulnerable module: node-fetch
  • Introduced through: node-fetch@2.6.1

Detailed paths

  • Introduced through: moleculer-sharp@designtesbrot/moleculer-sharp#868dbefa0a9b579ae2acaf0cd571aa6e48dc3368 › node-fetch@2.6.1
    Remediation: Upgrade to node-fetch@2.6.7.

Overview

node-fetch is a light-weight module that brings window.fetch to node.js

Affected versions of this package are vulnerable to Information Exposure when fetching a remote url with Cookie, if it get a Location response header, it will follow that url and try to fetch that url with provided cookie. This can lead to forwarding secure headers to 3th party.

Remediation

Upgrade node-fetch to version 2.6.7, 3.1.1 or higher.

References

Information Exposure vulnerability report

medium severity

Remote Code Execution (RCE)

  • Vulnerable module: sharp
  • Introduced through: sharp@0.27.2

Detailed paths

  • Introduced through: moleculer-sharp@designtesbrot/moleculer-sharp#868dbefa0a9b579ae2acaf0cd571aa6e48dc3368 › sharp@0.27.2
    Remediation: Upgrade to sharp@0.30.5.

Overview

sharp is a High performance Node.js image processing, the fastest module to resize JPEG, PNG, WebP, GIF, AVIF and TIFF images

Affected versions of this package are vulnerable to Remote Code Execution (RCE). There is a possible vulnerability in logic that is run only at npm install time when installing the package. If an attacker has the ability to set the value of the PKG_CONFIG_PATH environment variable in a build environment then they might be able to use this to inject an arbitrary command at npm install time. This is not part of any runtime code and does not affect Windows users at all.

Remediation

Upgrade sharp to version 0.30.5 or higher.

References

Remote Code Execution (RCE) vulnerability report

medium severity

Regular Expression Denial of Service (ReDoS)

  • Vulnerable module: ramda
  • Introduced through: ramda@0.27.1

Detailed paths

  • Introduced through: moleculer-sharp@designtesbrot/moleculer-sharp#868dbefa0a9b579ae2acaf0cd571aa6e48dc3368 › ramda@0.27.1
    Remediation: Upgrade to ramda@0.27.2.

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in source/trim.js within variable ws.

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 ramda to version 0.27.2 or higher.

References

Regular Expression Denial of Service (ReDoS) vulnerability report