Allocation of Resources Without Limits or Throttling
Detailed paths
Overview
Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to improper parsing of malformed tokens which can lead to memory consumption.
Remediation
Upgrade golang.org/x/oauth2/jws to version 0.27.0 or higher.
References
Allocation of Resources Without Limits or Throttling
Detailed paths
Overview
Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the use of strings.Split to split JWT tokens. An attacker can cause memory exhaustion and service disruption by sending numerous malformed tokens with a large number of . characters.
Workaround
This vulnerability can be mitigated by pre-validating that payloads passed to Go JOSE do not contain an excessive number of . characters.
Remediation
Upgrade github.com/go-jose/go-jose/v4 to version 4.0.5 or higher.
References
Allocation of Resources Without Limits or Throttling
Detailed paths
Overview
Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling due to the use of strings.Split to split JWT tokens. An attacker can cause memory exhaustion and service disruption by sending numerous malformed tokens with a large number of . characters.
Workaround
This vulnerability can be mitigated by pre-validating that payloads passed to Go JOSE do not contain an excessive number of . characters.
Remediation
Upgrade github.com/go-jose/go-jose/v3 to version 3.0.4 or higher.
References
Regular Expression Denial of Service (ReDoS)
Detailed paths
Overview
foundation-sites is a responsive front-end framework
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to inefficient backtracking in the regular expressions used in URL forms.
PoC
https://www.''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
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:
AThe 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.DFinally, 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:
- CCC
- CC+C
- C+CC
- 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
There is no fixed version for foundation-sites.
References
Regular Expression Denial of Service (ReDoS)
Detailed paths
Overview
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in the replace() method in wrapRegExp.js. An attacker can cause degradation in performance by supplying input strings that exploit the quadratic complexity of the replacement algorithm.
This is only exploitable when all of the following conditions are met:
The code passes untrusted strings in the second argument to
.replace().The compiled regular expressions being applied contain named capture groups.
In the case of @babel/preset-env, if the targets option is in use the application will be vulnerable under either of the following conditions:
A browser older than Chrome 64, Opera 71, Edge 79, Firefox 78, Safari 11.1, or Node.js 10 is used when processing named capture groups.
A browser older than Chrome/Edge 126, Opera 112, Firefox 129, Safari 17.4, or Node.js 23 is used when processing duplicated named capture groups.
Note: The project maintainers advise that "just updating your Babel dependencies is not enough: you will also need to re-compile your code."
Workaround
This vulnerability can be avoided by filtering out input containing a $< that is not followed by a >.
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:
AThe 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.DFinally, 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:
- CCC
- CC+C
- C+CC
- 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 @babel/runtime to version 7.26.10, 8.0.0-alpha.17 or higher.
References
Arbitrary Code Injection
Detailed paths
Overview
prismjs is a lightweight, robust, elegant syntax highlighting library.
Affected versions of this package are vulnerable to Arbitrary Code Injection via the document.currentScript lookup process. An attacker can manipulate the web page content and execute unintended actions by injecting HTML elements that overshadow legitimate DOM elements.
Note:
This is only exploitable if the application accepts untrusted input containing HTML but not direct JavaScript.
Remediation
Upgrade prismjs to version 1.30.0 or higher.
References
Insecure Randomness
Detailed paths
Overview
Affected versions of this package are vulnerable to Insecure Randomness due to its use of the hexoid() function in the generation of fingerprint IDs.
Remediation
Upgrade formidable to version 2.1.3, 3.5.3 or higher.
References
Regular Expression Denial of Service (ReDoS)
Detailed paths
Overview
brace-expansion is a Brace expansion as known from sh/bash
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) in the expand() function, which is prone to catastrophic backtracking on very long malicious inputs.
PoC
import index from "./index.js";
let str = "{a}" + ",".repeat(100000) + "\u0000";
let startTime = performance.now();
const result = index(str);
let endTime = performance.now();
let timeTaken = endTime - startTime;
console.log(`匹配耗时: ${timeTaken.toFixed(3)} 毫秒`);
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:
AThe 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.DFinally, 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:
- CCC
- CC+C
- C+CC
- 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 brace-expansion to version 1.1.12, 2.0.2, 3.0.1, 4.0.1 or higher.