Safety CI > kwierman/pewpew

pyup-update-click-8.1.3-to-8.1.5

1 year, 9 months ago

7ca501f1

Update click from 8.1.3 to 8.1.5

No dependencies with known security vulnerabilities.

pyup-update-mock-5.0.2-to-5.1.0

1 year, 9 months ago

49871865

Update mock from 5.0.2 to 5.1.0

No dependencies with known security vulnerabilities.

pyup-update-numpy-1.24.3-to-1.25.1

1 year, 9 months ago

c34dd421

Update numpy from 1.24.3 to 1.25.1

No dependencies with known security vulnerabilities.

pyup-update-click-8.1.3-to-8.1.4

1 year, 9 months ago

8bdf654c

Update click from 8.1.3 to 8.1.4

No dependencies with known security vulnerabilities.

pyup-update-h5py-3.8.0-to-3.9.0

1 year, 10 months ago

98f20060

Update h5py from 3.8.0 to 3.9.0

celery 5.2.7 has known security vulnerabilities.

Package	Installed	Affected	Info

pyup-update-celery-5.2.7-to-5.3.1

1 year, 10 months ago

3035c14d

Update celery from 5.2.7 to 5.3.1

No dependencies with known security vulnerabilities.

pyup-update-sphinx_rtd_theme-1.2.1-to-1.2.2

1 year, 10 months ago

6a3c7240

Update sphinx_rtd_theme from 1.2.1 to 1.2.2

No dependencies with known security vulnerabilities.

pyup-update-numpy-1.24.3-to-1.25.0

1 year, 10 months ago

d23d85ae

Update numpy from 1.24.3 to 1.25.0

No dependencies with known security vulnerabilities.

pyup-update-celery-5.2.7-to-5.3.0

1 year, 10 months ago

af611e04

Update celery from 5.2.7 to 5.3.0

No dependencies with known security vulnerabilities.

master

1 year, 10 months ago

7e9c9446

Update requirements_doc.txt

No dependencies with known security vulnerabilities.

master

1 year, 10 months ago

934b150c

Update requirements_dev.txt

No dependencies with known security vulnerabilities.

master

1 year, 10 months ago

5499f79c

Update requirements.txt

No dependencies with known security vulnerabilities.

pyup-update-sphinx_rtd_theme-0.5.1-to-1.2.1

1 year, 10 months ago

79766f67

Update sphinx_rtd_theme from 0.5.1 to 1.2.1

click 7.1.2, celery 5.0.5 and numpy 1.20.0 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.1.2	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
celery	5.0.5	<5.2.0	show Celery 5.2.0 updates 'kombu' to v5.2.1, which includes dependencies updates that resolve security issues.
celery	5.0.5	<5.2.2	show Celery 5.2.2 includes a fix for CVE-2021-23727: Celery before 5.2.2. by default trusts the messages and metadata stored in backends (result stores). When reading task metadata from the backend, the data is deserialized. Given that an attacker can gain access to, or somehow manipulate the metadata within a celery backend, they could trigger a stored command injection vulnerability and potentially gain further access to the system.
numpy	1.20.0	<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	1.20.0	<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.
numpy	1.20.0	<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
numpy	1.20.0	<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

pyup-update-sphinx-3.4.3-to-7.0.1

1 year, 11 months ago

cce7399e

Update sphinx from 3.4.3 to 7.0.1

click 7.1.2, celery 5.0.5 and numpy 1.20.0 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.1.2	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
celery	5.0.5	<5.2.0	show Celery 5.2.0 updates 'kombu' to v5.2.1, which includes dependencies updates that resolve security issues.
celery	5.0.5	<5.2.2	show Celery 5.2.2 includes a fix for CVE-2021-23727: Celery before 5.2.2. by default trusts the messages and metadata stored in backends (result stores). When reading task metadata from the backend, the data is deserialized. Given that an attacker can gain access to, or somehow manipulate the metadata within a celery backend, they could trigger a stored command injection vulnerability and potentially gain further access to the system.
numpy	1.20.0	<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	1.20.0	<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.
numpy	1.20.0	<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
numpy	1.20.0	<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

pyup-update-sphinx-3.4.3-to-7.0.0

1 year, 11 months ago

a77e096f

Update sphinx from 3.4.3 to 7.0.0

click 7.1.2, celery 5.0.5 and numpy 1.20.0 have known security vulnerabilities.

Package	Installed	Affected	Info
click	7.1.2	<8.0.0	show Click 8.0.0 uses 'mkstemp()' instead of the deprecated & insecure 'mktemp()'. https://github.com/pallets/click/issues/1752
celery	5.0.5	<5.2.0	show Celery 5.2.0 updates 'kombu' to v5.2.1, which includes dependencies updates that resolve security issues.
celery	5.0.5	<5.2.2	show Celery 5.2.2 includes a fix for CVE-2021-23727: Celery before 5.2.2. by default trusts the messages and metadata stored in backends (result stores). When reading task metadata from the backend, the data is deserialized. Given that an attacker can gain access to, or somehow manipulate the metadata within a celery backend, they could trigger a stored command injection vulnerability and potentially gain further access to the system.
numpy	1.20.0	<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	1.20.0	<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.
numpy	1.20.0	<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
numpy	1.20.0	<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