Find, fix and prevent vulnerabilities in your code.

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

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 Improper Handling of Unicode Encoding in the process that applies exclusion directives from MANIFEST.in due to improper Unicode normalization handling on macOS APFS or HFS+ filesystems. An attacker can cause unintended files to be included in a source distribution by creating filenames that exploit normalization collisions.

Remediation

Upgrade setuptools to version 83.0.0 or higher.

References

• GitHub Advisory
• 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 Directory Traversal through the ‎PackageIndex._download_url method. Due to insufficient sanitization of special characters, an attacker can write files to arbitrary locations on the filesystem with the permissions of the process running the Python code. In certain scenarios, an attacker could potentially escalate to remote code execution by leveraging malicious URLs present in a package index.

PoC

python poc.py
# Payload file: http://localhost:8000/%2fhome%2fuser%2f.ssh%2fauthorized_keys
# Written to: /home/user/.ssh/authorized_keys

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 setuptools to version 78.1.1 or higher.

References

• GitHub Commit
• GitHub Issue
• Vulnerable Code

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 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

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 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