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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 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

Overview

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via crafted HTML package or custom PackageIndex page.

Note:

Only a small portion of the user base is impacted by this flaw. Setuptools maintainers pointed out that package_index is deprecated (not formally, but “in spirit”) and the vulnerability isn't reachable through standard, recommended workflows.

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 setuptools to version 65.5.1 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub Release
• Pyup Blog
• Vulnerable Code

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

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

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the SUBAUTHORITY_PAT regex pattern in src/urllib3/util/url.py.

If a URL is passed as a parameter or redirected to via an HTTP redirect and it contains many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service.

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 urllib3 to version 1.26.5 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 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