Vulnerabilities

25 via 64 paths

Dependencies

18

Source

GitHub

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Severity
  • 1
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Status
  • 25
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critical severity

Heap-based Buffer Overflow

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Pillow is a PIL (Python Imaging Library) fork.

Affected versions of this package are vulnerable to Heap-based Buffer Overflow when the ReadHuffmanCodes() function is used. An attacker can craft a special WebP lossless file that triggers the ReadHuffmanCodes() function to allocate the HuffmanCode buffer with a size that comes from an array of precomputed sizes: kTableSize. The color_cache_bits value defines which size to use. The kTableSize array only takes into account sizes for 8-bit first-level table lookups but not second-level table lookups. libwebp allows codes that are up to 15-bit (MAX_ALLOWED_CODE_LENGTH). When BuildHuffmanTable() attempts to fill the second-level tables it may write data out-of-bounds. The OOB write to the undersized array happens in ReplicateValue.

Notes:

This is only exploitable if the color_cache_bits value defines which size to use.

This vulnerability was also published on libwebp CVE-2023-5129

Changelog:

2023-09-12: Initial advisory publication

2023-09-27: Advisory details updated, including CVSS, references

2023-09-27: CVE-2023-5129 rejected as a duplicate of CVE-2023-4863

2023-09-28: Research and addition of additional affected libraries

2024-01-28: Additional fix information

Remediation

Upgrade Pillow to version 10.0.1 or higher.

References

high severity

Out-of-bounds Write

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Out-of-bounds Write in the ImageFilter.RankFilter process when a very large odd filter size is provided, leading to unchecked signed integer arithmetic in ImagingExpand. An attacker can cause a heap out-of-bounds write by supplying crafted input to the filter size parameter.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

high severity

Allocation of Resources Without Limits or Throttling

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling in the decode function of PdfStream in PdfParser, where zlib decompression is performed without limiting the output size. An attacker can cause excessive memory consumption by submitting a specially crafted PDF file with a maliciously large Length field.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

high severity

Memory Allocation with Excessive Size Value

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Memory Allocation with Excessive Size Value via the PcfFontFile._load_bitmaps process. An attacker can cause excessive memory allocation by supplying crafted PCF font data that bypasses decompression bomb checks.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

high severity

Memory Allocation with Excessive Size Value

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Memory Allocation with Excessive Size Value in the FontFile.compile process. An attacker can cause excessive memory allocation by providing a specially crafted font file that triggers uncontrolled resource consumption during image conversion or saving.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

high severity

Memory Allocation with Excessive Size Value

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Memory Allocation with Excessive Size Value in the bdf_char process when attacker-controlled dimensions from a BDF font file are passed to Image.new() without invoking the _decompression_bomb_check function. An attacker can cause excessive memory allocation by supplying specially crafted font files.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

high severity

Memory Allocation with Excessive Size Value

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Memory Allocation with Excessive Size Value in the GdImageFile._open process. An attacker can cause excessive memory allocation by supplying a crafted .gd file that bypasses decompression bomb checks.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

high severity

Out-of-bounds Write

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Out-of-bounds Write via the apply function. An attacker can cause memory corruption and potentially crash the application by providing an output image with a mode that does not match the transform's declared output mode.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

high severity

Arbitrary Code Execution

  • Vulnerable module: fonttools
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 fonttools@4.38.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 fonttools@4.38.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

fonttools is a Tools to manipulate font files

Affected versions of this package are vulnerable to Arbitrary Code Execution due to the parseBlendList() function's usage of built-in Python's eval() function when parsing TTX font data. An attacker can execute arbitrary scripts by supplying a specially crafted numeric value strings to TTX XML attributes.

Remediation

Upgrade fonttools to version 4.62.0 or higher.

References

high severity

Out-of-bounds Read

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Out-of-bounds Read in the TGA RLE encoder process. An attacker can access sensitive heap data and potentially cause limited denial of service by crafting a specially designed image file.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References

high severity

Eval Injection

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Eval Injection via the PIL.ImageMath.eval function when an attacker has control over the keys passed to the environment argument.

PoC

from PIL import Image, ImageMath

image1 = Image.open('__class__')
image2 = Image.open('__bases__')
image3 = Image.open('__subclasses__')
image4 = Image.open('load_module')
image5 = Image.open('system')

expression = "().__class__.__bases__[0].__subclasses__()[104].load_module('os').system('whoami')"

environment = {
    image1.filename: image1,
    image2.filename: image2,
    image3.filename: image3,
    image4.filename: image4,
    image5.filename: image5
}

ImageMath.eval(expression, **environment)

Remediation

Upgrade pillow to version 10.2.0 or higher.

References

high severity

XML External Entity (XXE) Injection

  • Vulnerable module: fonttools
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 fonttools@4.38.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 fonttools@4.38.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

fonttools is a Tools to manipulate font files

Affected versions of this package are vulnerable to XML External Entity (XXE) Injection via the OT-SVG parser in the svg.py file.

Details

XXE Injection is a type of attack against an application that parses XML input. XML is a markup language that defines a set of rules for encoding documents in a format that is both human-readable and machine-readable. By default, many XML processors allow specification of an external entity, a URI that is dereferenced and evaluated during XML processing. When an XML document is being parsed, the parser can make a request and include the content at the specified URI inside of the XML document.

Attacks can include disclosing local files, which may contain sensitive data such as passwords or private user data, using file: schemes or relative paths in the system identifier.

For example, below is a sample XML document, containing an XML element- username.

<xml>
<?xml version="1.0" encoding="ISO-8859-1"?>
   <username>John</username>
</xml>

An external XML entity - xxe, is defined using a system identifier and present within a DOCTYPE header. These entities can access local or remote content. For example the below code contains an external XML entity that would fetch the content of /etc/passwd and display it to the user rendered by username.

<xml>
<?xml version="1.0" encoding="ISO-8859-1"?>
<!DOCTYPE foo [
   <!ENTITY xxe SYSTEM "file:///etc/passwd" >]>
   <username>&xxe;</username>
</xml>

Other XXE Injection attacks can access local resources that may not stop returning data, possibly impacting application availability and leading to Denial of Service.

Remediation

Upgrade fonttools to version 4.43.0 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Denial of Service (DoS) when using arbitrary strings as text input and the number of characters passed into PIL.ImageFont.ImageFont.getmask() is over a certain limit. This can lead to a system crash.

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 pillow to version 10.2.0 or higher.

References

high severity

Denial of Service (DoS)

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Denial of Service (DoS) if the size of individual glyphs extends beyond the bitmap image, when using PIL.ImageFont.ImageFont function. Exploiting this vulnerability could lead to a system crash.

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 pillow to version 10.2.0 or higher.

References

high severity

Uncontrolled Resource Consumption ('Resource Exhaustion')

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Uncontrolled Resource Consumption ('Resource Exhaustion') when the ImageFont truetype in an ImageDraw instance operates on a long text argument. An attacker can cause the service to crash by processing a task that uncontrollably allocates memory.

Remediation

Upgrade pillow to version 10.0.0 or higher.

References

medium severity

XML Injection

  • Vulnerable module: fonttools
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 fonttools@4.38.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 fonttools@4.38.0
    Remediation: Upgrade to seaborn@0.12.2.

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

medium severity

Infinite loop

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

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

medium severity

Infinite loop

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

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

medium severity

Buffer Overflow

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

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

medium severity

Storage of Sensitive Data in a Mechanism without Access Control

  • Vulnerable module: scikit-learn
  • Introduced through: scikit-learn@1.0.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data scikit-learn@1.0.2
    Remediation: Upgrade to scikit-learn@1.5.0.

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

medium severity

Integer Overflow or Wraparound

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

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

low severity

Buffer Overflow

  • Vulnerable module: numpy
  • Introduced through: numpy@1.21.3, matplotlib@3.5.3 and others

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data numpy@1.21.3
    Remediation: Upgrade to numpy@1.22.0.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 numpy@1.21.3
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data pandas@1.3.5 numpy@1.21.3
    Remediation: Upgrade to pandas@2.1.0.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data scikit-learn@1.0.2 numpy@1.21.3
    Remediation: Upgrade to scikit-learn@1.3.1.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 pandas@1.3.5 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.

Overview

numpy is a fundamental package needed for scientific computing with Python.

Affected versions of this package are vulnerable to Buffer Overflow due to missing boundary checks in the array_from_pyobj function of fortranobject.c. This may allow an attacker to conduct Denial of Service by carefully constructing an array with negative values.

Remediation

Upgrade numpy to version 1.22.0 or higher.

References

low severity

Denial of Service (DoS)

  • Vulnerable module: numpy
  • Introduced through: numpy@1.21.3, matplotlib@3.5.3 and others

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data numpy@1.21.3
    Remediation: Upgrade to numpy@1.22.0.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 numpy@1.21.3
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data pandas@1.3.5 numpy@1.21.3
    Remediation: Upgrade to pandas@2.1.0.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data scikit-learn@1.0.2 numpy@1.21.3
    Remediation: Upgrade to scikit-learn@1.3.1.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 pandas@1.3.5 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.

Overview

numpy is a fundamental package needed for scientific computing with Python.

Affected versions of this package are vulnerable to Denial of Service (DoS) due to an incomplete string comparison in the numpy.core component, which may allow attackers to fail the APIs via constructing specific string objects.

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 numpy to version 1.22.0rc1 or higher.

References

low severity

NULL Pointer Dereference

  • Vulnerable module: numpy
  • Introduced through: numpy@1.21.3, matplotlib@3.5.3 and others

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data numpy@1.21.3
    Remediation: Upgrade to numpy@1.22.2.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 numpy@1.21.3
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data pandas@1.3.5 numpy@1.21.3
    Remediation: Upgrade to pandas@2.1.0.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data scikit-learn@1.0.2 numpy@1.21.3
    Remediation: Upgrade to scikit-learn@1.3.1.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 pandas@1.3.5 numpy@1.21.3
    Remediation: Upgrade to seaborn@0.12.2.

Overview

numpy is a fundamental package needed for scientific computing with Python.

Affected versions of this package are vulnerable to NULL Pointer Dereference due to missing return-value validation in the PyArray_DescrNew function, which may allow attackers to conduct Denial of Service attacks by repetitively creating and sort arrays.

Note: This may likely only happen if application memory is already exhausted, as it requires the newdescr object of the PyArray_DescrNew to evaluate to NULL.

Remediation

Upgrade numpy to version 1.22.2 or higher.

References

low severity

Command Injection

  • Vulnerable module: pillow
  • Introduced through: matplotlib@3.5.3 and seaborn@0.12.2

Detailed paths

  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to matplotlib@3.5.3.
  • Introduced through: mramshaw/ML_with_Missing_Data@mramshaw/ML_with_Missing_Data seaborn@0.12.2 matplotlib@3.5.3 pillow@9.5.0
    Remediation: Upgrade to seaborn@0.12.2.

Overview

Affected versions of this package are vulnerable to Command Injection via the get_command function. An attacker can execute arbitrary commands by supplying a file path containing shell metacharacters.

Remediation

Upgrade pillow to version 12.3.0 or higher.

References