Snyk test report

LGPL-3.0 license

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LGPL-3.0 license

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Improper Validation of Syntactic Correctness of Input

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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 Improper Validation of Syntactic Correctness of Input in the tokenizer in token.go, which incorrectly interprets tags as closing tags, allowing malicious input to be incorrectly processed and the DOM to be corrupted.

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 golang.org/x/net/html to version 0.38.0 or higher.

References

• GitHub Commit
• GitHub Issue
• Google Groups Announcement

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Unexpected Status Code or Return Value

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Overview

Affected versions of this package are vulnerable to Unexpected Status Code or Return Value in initConn(), which causes out of order responses when CLIENT SETINFO times out while establishing a connection.

Workaround

This vulnerability can be avoided by setting DisableIndentity to true when initializing a client.

Remediation

Upgrade github.com/redis/go-redis/v9 to version 9.5.5, 9.6.3, 9.7.2 or higher.

References

• GitHub Commit
• GitHub PR

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MPL-2.0 license

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MPL-2.0 license

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MPL-2.0 license

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MPL-2.0 license

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MPL-2.0 license

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MPL-2.0 license

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MPL-2.0 license

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MPL-2.0 license

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MPL-2.0 license

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MPL-2.0 license

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Allocation of Resources Without Limits or Throttling

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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

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Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition')

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Overview

github.com/Azure/azure-sdk-for-go/sdk/azidentity is a module that provides Microsoft Entra ID (formerly Azure Active Directory) token authentication support across the Azure SDK. It includes a set of TokenCredential implementations, which can be used with Azure SDK clients supporting token authentication.

Affected versions of this package are vulnerable to Concurrent Execution using Shared Resource with Improper Synchronization ('Race Condition') in the authentication process. An attacker can elevate privileges by exploiting race conditions during the token validation steps. This is only exploitable if the application is configured to use multiple threads or processes for handling authentication requests.

Notes:

1. An attacker who successfully exploited the vulnerability could elevate privileges and read any file on the file system with SYSTEM access permissions;

2. An attacker who successfully exploits this vulnerability can only obtain read access to the system files by exploiting this vulnerability. The attacker cannot perform write or delete operations on the files;

3. The vulnerability exists in the following credential types: DefaultAzureCredential and ManagedIdentityCredential;

4. The vulnerability exists in the following credential types:

ManagedIdentityApplication (.NET)

ManagedIdentityApplication (Java)

ManagedIdentityApplication (Node.js)

Remediation

Upgrade github.com/Azure/azure-sdk-for-go/sdk/azidentity to version 1.6.0 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub Commit
• Microsoft Advisory

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Regular Expression Denial of Service (ReDoS)

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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

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Regular Expression Denial of Service (ReDoS)

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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:

1. The code passes untrusted strings in the second argument to .replace().

2. 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:

1. 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.

2. 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:

• 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 @babel/runtime to version 7.26.10, 8.0.0-alpha.17 or higher.

References

• GitHub Commit
• GitHub Gist
• GitHub PR

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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