Snyk test report

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

$ 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
        

const { XMLParser } = require('fast-xml-parser');
        
        const entity = 'A'.repeat(1000);
        const refs = '&big;'.repeat(100);
        const xml = `<!DOCTYPE foo [<!ENTITY big "${entity}">]><root>${refs}</root>`;
        
        console.time('parse');
        new XMLParser().parse(xml);
        console.timeEnd('parse');
        

const { XMLParser } = require("fast-xml-parser");
        
        // Developer intends: "no entities allowed at all"
        const parser = new XMLParser({
          processEntities: {
            enabled: true,
            maxEntityCount: 0,    // should mean "zero entities allowed"
            maxEntitySize: 0       // should mean "zero-length entities only"
          }
        });
        
        // Generate XML with many large entities
        let entities = "";
        for (let i = 0; i < 1000; i++) {
          entities += `<!ENTITY e${i} "${"A".repeat(100000)}">`;
        }
        
        const xml = `<?xml version="1.0"?>
        <!DOCTYPE foo [
          ${entities}
        ]>
        <foo>&e0;</foo>`;
        
        // This should throw "Entity count exceeds maximum" but does not
        try {
          const result = parser.parse(xml);
          console.log("VULNERABLE: parsed without error, entities bypassed limits");
        } catch (e) {
          console.log("SAFE:", e.message);
        }
        
        // Control test: setting maxEntityCount to 1 correctly blocks
        const safeParser = new XMLParser({
          processEntities: {
            enabled: true,
            maxEntityCount: 1,
            maxEntitySize: 100
          }
        });
        
        try {
          safeParser.parse(xml);
          console.log("ERROR: should have thrown");
        } catch (e) {
          console.log("CONTROL:", e.message);  // "Entity count (2) exceeds maximum allowed (1)"
        }
        

Uncontrolled Recursion

---

Overview

Affected versions of this package are vulnerable to Uncontrolled Recursion in the compose/resolve phase due to using recursive function calls without a depth bound. An attacker can cause the application to throw a RangeError and potentially terminate the Node.js process by supplying a deeply nested YAML payload that exhausts the call stack.

PoC

const YAML = require('yaml');
        
        // ~10 KB payload: 5000 levels of nested flow sequences
        const payload = '['.repeat(5000) + '1' + ']'.repeat(5000);
        
        try {
          YAML.parse(payload);
        } catch (e) {
          console.log(e.constructor.name); // RangeError (NOT YAMLParseError)
          console.log(e.message);          // Maximum call stack size exceeded
        }
        

Remediation

Upgrade yaml to version 1.10.3, 2.8.3 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub Release
• GitHub Release

---

Inefficient Algorithmic Complexity

---

Overview

golang.org/x/net/html is a package that implements an HTML5-compliant tokenizer and parser.

Affected versions of this package are vulnerable to Inefficient Algorithmic Complexity via the html.Parse function due to quadratic parsing complexity when processing certain inputs, which can lead to denial of service (DoS) if an attacker provides specially crafted HTML content.

Remediation

Upgrade golang.org/x/net/html to version 0.45.0 or higher.

References

• GitHub Issue
• Go Advisory
• Go Commit
• Google Groups Forum

---

Infinite loop

---

Overview

golang.org/x/net/html is a package that implements an HTML5-compliant tokenizer and parser.

Affected versions of this package are vulnerable to Infinite loop via the html.Parse function. An attacker can cause resource exhaustion and disrupt service availability by submitting specially crafted HTML input that triggers an infinite parsing loop.

Remediation

Upgrade golang.org/x/net/html to version 0.45.0 or higher.

References

• GitHub Issue
• Go Advisory
• Go Commit
• Google Groups Forum

---

Improper Validation of Specified Type of Input

---

Overview

Affected versions of this package are vulnerable to Improper Validation of Specified Type of Input in the calls plugin when handling websocket messages containing malformed msgpack frames. An attacker can cause the server to consume excessive memory and crash by sending specially crafted websocket requests.

Remediation

There is no fixed version for github.com/vmihailenco/msgpack/v5.

References

• GitHub Commit
• Mattermost Security Updates

---

MPL-2.0 license

---

MPL-2.0 license

---

MPL-2.0 license

---

MPL-2.0 license

---

MPL-2.0 license

---

MPL-2.0 license

---

MPL-2.0 license

---

MPL-2.0 license

---

MPL-2.0 license

---

MPL-2.0 license

---

Improper Handling of Highly Compressed Data (Data Amplification)

---

Overview

Affected versions of this package are vulnerable to Improper Handling of Highly Compressed Data (Data Amplification). An attacker could send a JWE containing compressed data that, when decompressed by Decrypt or DecryptMulti, would use large amounts of memory and CPU.

Remediation

Upgrade github.com/go-jose/go-jose/v3 to version 3.0.3 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Commit

---

Allocation of Resources Without Limits or Throttling

---

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

• GitHub Commit
• GitHub Release

---

Improper Validation of Integrity Check Value

---

Overview

Affected versions of this package are vulnerable to Improper Validation of Integrity Check Value for .idx and .pack files. An attacker can cause the application to consume corrupted files, leading to unexpected errors, due to checksums not being checked in the loadIdxFile() function.

Workaround

This vulnerability can be mitigated by running 'git fsck' from the git CLI to check for data corruption on a given repository.

Remediation

Upgrade github.com/go-git/go-git/v5/storage/filesystem to version 5.16.5 or higher.

References

• GitHub Commit
• GitHub Release

---

Regular Expression Denial of Service (ReDoS)

---

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:

• 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

There is no fixed version for foundation-sites.

References

• GitHub Advisory
• GitHub Issue
• Vulnerable Code

---

Buffer Overflow

---

Overview

fast-xml-parser is a Validate XML, Parse XML, Build XML without C/C++ based libraries

Affected versions of this package are vulnerable to Buffer Overflow via the XMLBuilder when preserveOrder:true is set. An attacker can cause the application to crash by providing specially crafted input data.

Workaround

This vulnerability can be mitigated by using preserveOrder:false or by validating input data before passing it to the builder.

Remediation

Upgrade fast-xml-parser to version 4.5.4, 5.3.8 or higher.

References

• GitHub Commit
• GitHub PR

---

Cross-site Scripting (XSS)

---

Overview

dompurify is a DOM-only XSS sanitizer for HTML, MathML and SVG.

Affected versions of this package are vulnerable to Cross-site Scripting (XSS) in the createDOMPurify() function, via comments embedded in XML noscript, xmp, noembed, noframes, and iframe attributes containing scripts.

Details

Cross-site scripting (or XSS) is a code vulnerability that occurs when an attacker “injects” a malicious script into an otherwise trusted website. The injected script gets downloaded and executed by the end user’s browser when the user interacts with the compromised website.

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 dompurify to version 2.5.9, 3.3.2 or higher.

References

• GitHub Commit

---

Cross-site Scripting (XSS)

---

Overview

dompurify is a DOM-only XSS sanitizer for HTML, MathML and SVG.

Affected versions of this package are vulnerable to Cross-site Scripting (XSS) in the createDOMPurify() function, via comments embedded in XML textarea attributes containing scripts.

Details

Cross-site scripting (or XSS) is a code vulnerability that occurs when an attacker “injects” a malicious script into an otherwise trusted website. The injected script gets downloaded and executed by the end user’s browser when the user interacts with the compromised website.

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 dompurify to version 3.2.7 or higher.

References

• GitHub Commit

---

Regular Expression Denial of Service (ReDoS)

---

Overview

diff is a javascript text differencing implementation.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the parsePatch() and applyPatch() functions if the user input passed without sanitisation. An attacker can cause the process to enter an infinite loop and exhaust system memory by providing a patch with filename headers containing \r, \u2028, or \u2029 characters or having control over patch's patch header for application generated patches.

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 diff to version 3.5.1, 4.0.4, 5.2.2, 8.0.3 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Issue

---

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`);
        

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

$ 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