pyup-update-numpy-1.15.0-to-1.19.2
4 years, 10 months ago
Update numpy from 1.15.0 to 1.19.2
pip 18.0 and Sphinx 1.7.6 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| pip | 18.0 | <23.3 |
show Affected versions of Pip are vulnerable to Command Injection. When installing a package from a Mercurial VCS URL (ie "pip install hg+...") with pip prior to v23.3, the specified Mercurial revision could be used to inject arbitrary configuration options to the "hg clone" call (ie "--config"). Controlling the Mercurial configuration can modify how and which repository is installed. This vulnerability does not affect users who aren't installing from Mercurial. |
| pip | 18.0 | <21.1 |
show A flaw was found in python-pip in the way it handled Unicode separators in git references. A remote attacker could possibly use this issue to install a different revision on a repository. The highest threat from this vulnerability is to data integrity. |
| pip | 18.0 | <21.1 |
show An issue was discovered in Pip (all versions) because it installs the version with the highest version number, even if the user had intended to obtain a private package from a private index. This only affects use of the --extra-index-url option, and exploitation requires that the package does not already exist in the public index (and thus the attacker can put the package there with an arbitrary version number). A warning was added about this behavior in version 21.1. NOTE: it has been reported that this is intended functionality and the user is responsible for using --extra-index-url securely. |
| pip | 18.0 | <21.1 |
show Pip 21.1 updates its dependency 'urllib3' to v1.26.4 due to security issues. |
| pip | 18.0 | <19.2 |
show Versions of Pip prior to 19.2 are vulnerable to a directory traversal attack during the installation process from a URL. This vulnerability stems from improperly handling filenames in the Content-Disposition header that include path traversal sequences, potentially allowing unauthorized overwrite of critical files such as /root/.ssh/authorized_keys. The flaw is specifically found in the _download_http_url function within _internal/download.py. |
| pip | 18.0 | <25.0 |
show Pip solves a security vulnerability that previously allowed maliciously crafted wheel files to execute unauthorized code during installation. |
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
pyup-update-pip-18.0-to-20.2.3
4 years, 10 months ago
Update pip from 18.0 to 20.2.3
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-more_itertools-4.2.0-to-8.5.0
4 years, 10 months ago
Update more_itertools from 4.2.0 to 8.5.0
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-tox-3.1.2-to-3.20.0
4 years, 10 months ago
Update tox from 3.1.2 to 3.20.0
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-cryptography-2.3-to-3.1
4 years, 10 months ago
Update cryptography from 2.3 to 3.1
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-sphinx-1.7.6-to-3.2.1
4 years, 11 months ago
Update sphinx from 1.7.6 to 3.2.1
numpy 1.15.0 has known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-wheel-0.31.1-to-0.35.1
4 years, 11 months ago
Update wheel from 0.31.1 to 0.35.1
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-wheel-0.31.1-to-0.35.0
4 years, 11 months ago
Update wheel from 0.31.1 to 0.35.0
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-pip-18.0-to-20.2.2
4 years, 11 months ago
Update pip from 18.0 to 20.2.2
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-sphinx-1.7.6-to-3.2.0
4 years, 11 months ago
Update sphinx from 1.7.6 to 3.2.0
numpy 1.15.0 has known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-tox-3.1.2-to-3.19.0
4 years, 11 months ago
Update tox from 3.1.2 to 3.19.0
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-pip-18.0-to-20.2.1
4 years, 11 months ago
Update pip from 18.0 to 20.2.1
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-pytest-3.6.3-to-6.0.1
4 years, 11 months ago
Update pytest from 3.6.3 to 6.0.1
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-pytest-3.6.3-to-6.0.0
4 years, 11 months ago
Update pytest from 3.6.3 to 6.0.0
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
pyup-update-pip-18.0-to-20.2
4 years, 11 months ago
Update pip from 18.0 to 20.2
Sphinx 1.7.6 and numpy 1.15.0 have known security vulnerabilities.
| Package | Installed | Affected | Info |
|---|---|---|---|
| Sphinx | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| Sphinx | 1.7.6 | <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 | 1.7.6 | <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 | 1.7.6 | <3.0.4 |
show Sphinx 3.0.4 updates jQuery version from 3.4.1 to 3.5.1 for security reasons. |
| numpy | 1.15.0 | <1.16.3 |
show Numpy 1.16.3 includes a fix for CVE-2019-6446: It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: Third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources. https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb |
| numpy | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 | 1.15.0 | <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 |
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