pyup-update-sphinx_rtd_theme-0.5.1-to-1.1.1
3 years ago
Update sphinx_rtd_theme from 0.5.1 to 1.1.1
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 | <1.22.0 |
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 |
| 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. |
pyup-update-numpy-1.20.0-to-1.23.5
3 years ago
Update numpy from 1.20.0 to 1.23.5
click 7.1.2 and celery 5.0.5 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. |
pyup-update-sphinx-3.4.3-to-5.3.0
3 years, 1 month ago
Update sphinx from 3.4.3 to 5.3.0
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 | <1.22.0 |
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 |
| 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. |
pyup-update-numpy-1.20.0-to-1.23.4
3 years, 2 months ago
Update numpy from 1.20.0 to 1.23.4
click 7.1.2 and celery 5.0.5 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. |
pyup-update-sphinx-3.4.3-to-5.2.3
3 years, 2 months ago
Update sphinx from 3.4.3 to 5.2.3
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 | <1.22.0 |
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 |
| 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. |
pyup-update-sphinx-3.4.3-to-5.2.2
3 years, 2 months ago
Update sphinx from 3.4.3 to 5.2.2
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 | <1.22.0 |
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 |
| 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. |
pyup-update-sphinx-3.4.3-to-5.2.1
3 years, 2 months ago
Update sphinx from 3.4.3 to 5.2.1
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 | <1.22.0 |
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 |
| 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. |
pyup-update-sphinx-3.4.3-to-5.2.0.post0
3 years, 2 months ago
Update sphinx from 3.4.3 to 5.2.0.post0
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 | <1.22.0 |
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 |
| 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. |
pyup-update-numpy-1.20.0-to-1.23.3
3 years, 3 months ago
Update numpy from 1.20.0 to 1.23.3
click 7.1.2 and celery 5.0.5 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. |
pyup-update-numpy-1.20.0-to-1.23.2
3 years, 4 months ago
Update numpy from 1.20.0 to 1.23.2
click 7.1.2 and celery 5.0.5 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. |
pyup-update-sphinx-3.4.3-to-5.1.1
3 years, 4 months ago
Update sphinx from 3.4.3 to 5.1.1
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 | <1.22.0 |
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 |
| 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. |
pyup-update-sphinx-3.4.3-to-5.1.0
3 years, 4 months ago
Update sphinx from 3.4.3 to 5.1.0
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 | <1.22.0 |
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 |
| 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. |
pyup-update-numpy-1.20.0-to-1.23.1
3 years, 5 months ago
Update numpy from 1.20.0 to 1.23.1
click 7.1.2 and celery 5.0.5 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. |
pyup-update-numpy-1.20.0-to-1.23.0
3 years, 5 months ago
Update numpy from 1.20.0 to 1.23.0
click 7.1.2 and celery 5.0.5 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. |
dependabot/pip/numpy-1.22.0
3 years, 5 months ago
![@dependabot[bot]](https://avatars3.githubusercontent.com/u/49699333?v=3&s=32){kind=small}
Bump numpy from 1.20.0 to 1.22.0
Bumps [numpy](https://github.com/numpy/numpy) from 1.20.0 to 1.22.
click 7.1.2, celery 5.0.5 and numpy 1.22.0 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 | 1.22.0 | <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. |
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