Safety CI > py4ds/nbless

pyup-update-matplotlib-3.1.1-to-3.5.3

3 years, 2 months ago

Update matplotlib from 3.1.1 to 3.5.3

click 7.0, numpy 1.16.4, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-coverage-4.5.3-to-6.4.3

3 years, 3 months ago

2f5fbe7e

Update coverage from 4.5.3 to 6.4.3

click 7.0, numpy 1.16.4, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-sphinx-2.1.2-to-5.1.1

3 years, 3 months ago

216c370b

Update sphinx from 2.1.2 to 5.1.1

click 7.0, numpy 1.16.4, twine 1.13.0 and codecov 2.0.15 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-sphinx-2.1.2-to-5.1.0

3 years, 3 months ago

b85bfbe2

Update sphinx from 2.1.2 to 5.1.0

click 7.0, numpy 1.16.4, twine 1.13.0 and codecov 2.0.15 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

dependabot/pip/codecov-2.0.16

3 years, 3 months ago

47fe1d58

Bump codecov from 2.0.15 to 2.0.16 Bumps [codecov](https://github.com/codecov/codecov-python) from

click 7.0, numpy 1.16.4, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-coverage-4.5.3-to-6.4.2

3 years, 3 months ago

1013096d

Update coverage from 4.5.3 to 6.4.2

click 7.0, numpy 1.16.4, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-numpy-1.16.4-to-1.23.1

3 years, 4 months ago

6894865f

Update numpy from 1.16.4 to 1.23.1

click 7.0, Sphinx 2.1.2, twine 1.13.0 and codecov 2.0.15 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-sphinx-click-2.2.0-to-4.3.0

3 years, 4 months ago

09042723

Update sphinx-click from 2.2.0 to 4.3.0

click 7.0, numpy 1.16.4, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-tox-3.13.2-to-3.25.1

3 years, 4 months ago

477ec606

Update tox from 3.13.2 to 3.25.1

click 7.0, numpy 1.16.4, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-black-19.3b0-to-22.6.0

3 years, 4 months ago

30f78166

Update black from 19.3b0 to 22.6.0

click 7.0, numpy 1.16.4, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-numpy-1.16.4-to-1.23.0

3 years, 4 months ago

70855aa6

Update numpy from 1.16.4 to 1.23.0

click 7.0, Sphinx 2.1.2, twine 1.13.0 and codecov 2.0.15 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

dependabot/pip/numpy-1.22.0

3 years, 4 months ago

4bb811ea

Bump numpy from 1.16.4 to 1.22.0 Bumps [numpy](https://github.com/numpy/numpy) from 1.16.4 to 1.22.

click 7.0, numpy 1.22.0, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.22.0	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-sphinx-click-2.2.0-to-4.2.0

3 years, 4 months ago

b201968b

Update sphinx-click from 2.2.0 to 4.2.0

click 7.0, numpy 1.16.4, Sphinx 2.1.2 and 2 other dependencies have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in inventory. https://github.com/sphinx-doc/sphinx/issues/8175 https://github.com/sphinx-doc/sphinx/commit/f7b872e673f9b359a61fd287a7338a28077840d2
Sphinx	2.1.2	<3.3.0	show Sphinx 3.3.0 includes a fix for a ReDoS vulnerability in docstring. https://github.com/sphinx-doc/sphinx/issues/8172 https://github.com/sphinx-doc/sphinx/commit/f00e75278c5999f40b214d8934357fbf0e705417
Sphinx	2.1.2	<3.0.4	show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons.
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-sphinx-2.1.2-to-5.0.2

3 years, 4 months ago

eb872009

Update sphinx from 2.1.2 to 5.0.2

click 7.0, numpy 1.16.4, twine 1.13.0 and codecov 2.0.15 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.

pyup-update-sphinx-2.1.2-to-5.0.1

3 years, 5 months ago

1a632eb6

Update sphinx from 2.1.2 to 5.0.1

click 7.0, numpy 1.16.4, twine 1.13.0 and codecov 2.0.15 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.0	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
numpy	1.16.4	<1.21.0rc1	show Numpy 1.21.0rc1 includes a fix for CVE-2021-33430: A Buffer Overflow vulnerability 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 vulnerability. 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. https://github.com/numpy/numpy/issues/18939
numpy	1.16.4	<1.22.2	show Numpy 1.22.2 includes a fix for CVE-2021-41495: Null Pointer Dereference vulnerability exists in numpy.sort in NumPy in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS attacks by repetitively creating sort arrays. NOTE: While correct that validation is missing, an error can only occur due to an exhaustion of memory. If the user can exhaust memory, they are already privileged. Further, it should be practically impossible to construct an attack which can target the memory exhaustion to occur at exactly this place. NOTE2: The specs we include in this advisory differ from the publicly available on other sources. For example, the advisory posted by the NVD indicate that versions up to and including 1.19.0 are affected. However, research by Safety CLI Cybersecurity confirms that the vulnerability remained unaddressed until version 1.22.2.
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-34141: 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." https://github.com/numpy/numpy/issues/18993
numpy	1.16.4	<1.22.0	show Numpy 1.22.0 includes a fix for CVE-2021-41496: Buffer overflow in the array_from_pyobj function of fortranobject.c, 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). https://github.com/numpy/numpy/issues/19000
twine	1.13.0	<2.0.0	show Twine 2.0.0 updates requests to 2.20 (or later) to include a security fix.