tqa236 / auto-insurance Goto Github PK

Vulnerable Library - numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl

NumPy is the fundamental package for array computing with Python.

Library home page: https://files.pythonhosted.org/packages/3a/5f/47e578b3ae79e2624e205445ab77a1848acdaa2929a00eeef6b16eaaeb20/numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt

Dependency Hierarchy:

• xgboost-0.82-py2.py3-none-manylinux1_x86_64.whl (Root Library)
  • ❌ numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl (Vulnerable Library)

Found in base branch: main

Vulnerability Details

An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. NOTE: the vendor states that this reported code behavior is "completely harmless."
Mend Note: After conducting further research, Mend has determined that versions 1.12.0 through 1.21.6 of numpy are vulnerable to CVE-2021-34141

Publish Date: 2021-12-17

URL: CVE-2021-34141

CVSS 3 Score Details (5.3)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Network
  • Attack Complexity: Low
  • Privileges Required: None
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: None
  • Integrity Impact: None
  • Availability Impact: Low

Suggested Fix

Type: Upgrade version

Origin: https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-34141

Release Date: 2021-12-17

Fix Resolution: numpy - 1.22.0

Vulnerable Library - numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl

NumPy is the fundamental package for array computing with Python.

Library home page: https://files.pythonhosted.org/packages/3a/5f/47e578b3ae79e2624e205445ab77a1848acdaa2929a00eeef6b16eaaeb20/numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt

Dependency Hierarchy:

• xgboost-0.82-py2.py3-none-manylinux1_x86_64.whl (Root Library)
  • ❌ numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl (Vulnerable Library)

Found in base branch: main

Vulnerability Details

** DISPUTED ** Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative values. NOTE: The vendor does not agree this is a vulnerability; the negative dimensions can only be created by an already privileged user (or internally).
Mend Note: After conducting further research, Mend has determined that numpy versions before 1.22.0 are vulnerable to CVE-2021-41496

Publish Date: 2021-12-17

URL: CVE-2021-41496

CVSS 3 Score Details (5.5)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Local
  • Attack Complexity: Low
  • Privileges Required: Low
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: None
  • Integrity Impact: None
  • Availability Impact: High

Vulnerable Library - wheel-0.36.2-py2.py3-none-any.whl

A built-package format for Python

Library home page: https://files.pythonhosted.org/packages/65/63/39d04c74222770ed1589c0eaba06c05891801219272420b40311cd60c880/wheel-0.36.2-py2.py3-none-any.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt

Dependency Hierarchy:

• lightgbm-3.1.1-py2.py3-none-manylinux1_x86_64.whl (Root Library)
  • ❌ wheel-0.36.2-py2.py3-none-any.whl (Vulnerable Library)

Found in base branch: main

Vulnerability Details

An issue discovered in Python Packaging Authority (PyPA) Wheel 0.37.1 and earlier allows remote attackers to cause a denial of service via attacker controlled input to wheel cli.

Publish Date: 2022-12-23

URL: CVE-2022-40898

CVSS 3 Score Details (7.5)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Network
  • Attack Complexity: Low
  • Privileges Required: None
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: None
  • Integrity Impact: None
  • Availability Impact: High

Suggested Fix

Type: Upgrade version

Release Date: 2022-12-23

Fix Resolution: wheel 0.38.0

Vulnerable Library - setuptools-44.1.1-py2.py3-none-any.whl

Easily download, build, install, upgrade, and uninstall Python packages

Library home page: https://files.pythonhosted.org/packages/e1/b7/182161210a13158cd3ccc41ee19aadef54496b74f2817cc147006ec932b4/setuptools-44.1.1-py2.py3-none-any.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt

Dependency Hierarchy:

• catboost-0.24.4-cp27-none-manylinux1_x86_64.whl (Root Library)
  • matplotlib-2.2.5-cp27-cp27mu-manylinux1_x86_64.whl
    • kiwisolver-1.1.0-cp27-cp27mu-manylinux1_x86_64.whl
      • ❌ setuptools-44.1.1-py2.py3-none-any.whl (Vulnerable Library)

Found in base branch: main

Vulnerability Details

Python Packaging Authority (PyPA) setuptools before 65.5.1 allows remote attackers to cause a denial of service via HTML in a crafted package or custom PackageIndex page. There is a Regular Expression Denial of Service (ReDoS) in package_index.py.

Publish Date: 2022-12-23

URL: CVE-2022-40897

CVSS 3 Score Details (5.9)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Network
  • Attack Complexity: High
  • Privileges Required: None
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: None
  • Integrity Impact: None
  • Availability Impact: High

Suggested Fix

Type: Upgrade version

Origin: https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages/

Release Date: 2022-12-23

Fix Resolution: setuptools - 65.5.1

Vulnerable Library - pandas-0.24.2-cp27-cp27mu-manylinux1_x86_64.whl

Powerful data structures for data analysis, time series, and statistics

Library home page: https://files.pythonhosted.org/packages/db/83/7d4008ffc2988066ff37f6a0bb6d7b60822367dcb36ba5e39aa7801fda54/pandas-0.24.2-cp27-cp27mu-manylinux1_x86_64.whl

Path to dependency file: auto-insurance/requirements.txt

Path to vulnerable library: auto-insurance/requirements.txt

Dependency Hierarchy:

• catboost-0.24.4-cp27-none-manylinux1_x86_64.whl (Root Library)
  • ❌ pandas-0.24.2-cp27-cp27mu-manylinux1_x86_64.whl (Vulnerable Library)

Found in HEAD commit: 676813b44c111339797aeeba703ab92464642752

Found in base branch: main

Vulnerability Details

** DISPUTED ** pandas through 1.0.3 can unserialize and execute commands from an untrusted file that is passed to the read_pickle() function, if reduce makes an os.system call. NOTE: third parties dispute this issue because the read_pickle() function is documented as unsafe and it is the user's responsibility to use the function in a secure manner.

Publish Date: 2020-05-15

URL: CVE-2020-13091

CVSS 3 Score Details (9.8)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Network
  • Attack Complexity: Low
  • Privileges Required: None
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: High
  • Integrity Impact: High
  • Availability Impact: High

Suggested Fix

Type: Upgrade version

Origin: https://nvd.nist.gov/vuln/detail/CVE-2020-13091

Release Date: 2020-05-19

Fix Resolution: pandas - 0.3.0.beta,1.0.4;autovizwidget - 0.12.7;pandas - 1.0.4,1.1.0rc0

Vulnerable Library - numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl

NumPy is the fundamental package for array computing with Python.

Library home page: https://files.pythonhosted.org/packages/3a/5f/47e578b3ae79e2624e205445ab77a1848acdaa2929a00eeef6b16eaaeb20/numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl

Path to dependency file: /requirements.txt

Path to vulnerable library: /requirements.txt

Dependency Hierarchy:

• xgboost-0.82-py2.py3-none-manylinux1_x86_64.whl (Root Library)
  • ❌ numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl (Vulnerable Library)

Found in base branch: main

Vulnerability Details

** DISPUTED ** A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a malicious user cause a Denial of Service. NOTE: The vendor does not agree this is a vulneraility; In (very limited) circumstances a user may be able provoke the buffer overflow, the user is most likely already privileged to at least provoke denial of service by exhausting memory. Triggering this further requires the use of uncommon API (complicated structured dtypes), which is very unlikely to be available to an unprivileged user.
Mend Note: After conducting further research, Mend has determined that numpy versions before 1.21.0 are vulnerable to CVE-2021-33430

Publish Date: 2021-12-17

URL: CVE-2021-33430

CVSS 3 Score Details (5.3)

Base Score Metrics:

• Exploitability Metrics:
  • Attack Vector: Network
  • Attack Complexity: High
  • Privileges Required: Low
  • User Interaction: None
  • Scope: Unchanged
• Impact Metrics:
  • Confidentiality Impact: None
  • Integrity Impact: None
  • Availability Impact: High

Suggested Fix

Type: Upgrade version

Origin: https://nvd.nist.gov/vuln/detail/CVE-2021-33430

Release Date: 2021-12-17

Fix Resolution: numpy - 1.21.0