** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst

** DISPUTED ** Py throughout 1.11.0 allows remote attackers to conduct a ReDoS (Regular expression Denial of Service) attack via a Subversion repository with crafted info data because the InfoSvnCommand argument is mishandled.
https://github.com/pytest-dev/py/issues/287

Babel 2.9.1 includes a fix for CVE-2021-42771: Babel.Locale in Babel before 2.9.1 allows attackers to load arbitrary locale .dat files (containing serialized Python objects) via directory traversal, leading to code execution.
https://github.com/python-babel/babel/pull/782

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 1.22.0 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 1.21.0rc1 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 1.22.2  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.

Pyyaml version 5.4 includes a fix for CVE-2020-14343: A vulnerability was discovered in the PyYAML library in versions before 5.4, where it is susceptible to arbitrary code execution when it processes untrusted YAML files through the full_load method or with the FullLoader loader. Applications that use the library to process untrusted input may be vulnerable to this flaw. This flaw allows an attacker to execute arbitrary code on the system by abusing the python/object/new constructor. This flaw is due to an incomplete fix for CVE-2020-1747.
https://bugzilla.redhat.com/show_bug.cgi?id=1860466

Prior to 3.1.6, an oversight in how the Jinja sandboxed environment interacts with the |attr filter allows an attacker that controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to use the |attr filter to get a reference to a string's plain format method, bypassing the sandbox. After the fix, the |attr filter no longer bypasses the environment's attribute lookup. This vulnerability is fixed in 3.1.6.

An oversight in how the Jinja sandboxed environment detects calls to str.format allows an attacker who controls the content of a template to execute arbitrary Python code. To exploit the vulnerability, an attacker needs to control the content of a template. Whether that is the case depends on the type of application using Jinja. This vulnerability impacts users of applications which execute untrusted templates. Jinja's sandbox does catch calls to str.format and ensures they don't escape the sandbox. However, it's possible to store a reference to a malicious string's format method, then pass that to a filter that calls it. No such filters are built-in to Jinja, but could be present through custom filters in an application. After the fix, such indirect calls are also handled by the sandbox.

This affects the package jinja2 from 0.0.0 and before 2.11.3. The ReDoS vulnerability is mainly due to the '_punctuation_re regex' operator and its use of multiple wildcards. The last wildcard is the most exploitable as it searches for trailing punctuation. This issue can be mitigated by Markdown to format user content instead of the urlize filter, or by implementing request timeouts and limiting process memory.

Jinja is an extensible templating engine. The `xmlattr` filter in affected versions of Jinja accepts keys containing non-attribute characters. XML/HTML attributes cannot contain spaces, `/`, `>`, or `=`, as each would then be interpreted as starting a separate attribute. If an application accepts keys (as opposed to only values) as user input, and renders these in pages that other users see as well, an attacker could use this to inject other attributes and perform XSS. The fix for CVE-2024-22195 only addressed spaces but not other characters. Accepting keys as user input is now explicitly considered an unintended use case of the `xmlattr` filter, and code that does so without otherwise validating the input should be flagged as insecure, regardless of Jinja version. Accepting _values_ as user input continues to be safe.

Jinja is an extensible templating engine. Special placeholders in the template allow writing code similar to Python syntax. It is possible to inject arbitrary HTML attributes into the rendered HTML template, potentially leading to Cross-Site Scripting (XSS). The Jinja `xmlattr` filter can be abused to inject arbitrary HTML attribute keys and values, bypassing the auto escaping mechanism and potentially leading to XSS. It may also be possible to bypass attribute validation checks if they are blacklist-based.

Pygments 2.15.0 includes a fix for CVE-2022-40896: The regular expressions used when parsing Smithy, SQL/SQL+Jinja, and Java properties files were discovered to be vulnerable. As a result, pygmentizing a maliciously-crafted file of these kinds would have resulted in high resources consumption or crashing of the application.
https://pyup.io/posts/pyup-discovers-redos-vulnerabilities-in-top-python-packages-part-2

Pygments 2.7.4 includes a fix for CVE-2021-20270: An infinite loop in SMLLexer in Pygments versions 1.5 to 2.7.3 may lead to denial of service when performing syntax highlighting of a Standard ML (SML) source file, as demonstrated by input that only contains the "exception" keyword.

Pygments 2.7.4 includes a fix for CVE-2021-27291: In pygments 1.1+, fixed in 2.7.4, the lexers used to parse programming languages rely heavily on regular expressions. Some of the regular expressions have exponential or cubic worst-case complexity and are vulnerable to ReDoS. By crafting malicious input, an attacker can cause a denial of service.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to improper handling of highly compressed HTTP response bodies during streaming decompression. The urllib3.HTTPResponse methods stream(), read(), read1(), read_chunked(), and readinto() may fully decompress a minimal but highly compressed payload based on the Content-Encoding header into an internal buffer instead of limiting the decompressed output to the requested chunk size, causing excessive CPU usage and massive memory allocation on the client side.

Affected versions of urllib3 are vulnerable to an HTTP redirect handling vulnerability that fails to remove the HTTP request body when a POST changes to a GET via 301, 302, or 303 responses. This flaw can expose sensitive request data if the origin service is compromised and redirects to a malicious endpoint, though exploitability is low when no sensitive data is used. The vulnerability affects automatic redirect behavior. It is fixed in versions 1.26.18 and 2.0.7; update or disable redirects using redirects=False.
This vulnerability is specific to Python's urllib3 library.

Urllib3 1.26.5 includes a fix for CVE-2021-33503: When provided with a URL containing many @ characters in the authority component, the authority regular expression exhibits catastrophic backtracking, causing a denial of service if a URL were passed as a parameter or redirected to via an HTTP redirect.
https://github.com/advisories/GHSA-q2q7-5pp4-w6pg

Urllib3 1.26.17 and 2.0.5 include a fix for CVE-2023-43804: Urllib3 doesn't treat the 'Cookie' HTTP header special or provide any helpers for managing cookies over HTTP, that is the responsibility of the user. However, it is possible for a user to specify a 'Cookie' header and unknowingly leak information via HTTP redirects to a different origin if that user doesn't disable redirects explicitly.
https://github.com/urllib3/urllib3/security/advisories/GHSA-v845-jxx5-vc9f

urllib3 is a user-friendly HTTP client library for Python. Prior to 2.5.0, it is possible to disable redirects for all requests by instantiating a PoolManager and specifying retries in a way that disable redirects. By default, requests and botocore users are not affected. An application attempting to mitigate SSRF or open redirect vulnerabilities by disabling redirects at the PoolManager level will remain vulnerable. This issue has been patched in version 2.5.0.

Affected versions of the urllib3 package are vulnerable to Denial of Service (DoS) due to allowing an unbounded number of content-encoding decompression steps for HTTP responses. The HTTPResponse content decoding pipeline in urllib3 follows the Content-Encoding header and applies each advertised compression algorithm in sequence without enforcing a maximum chain length or effective output size, so a malicious peer can send a response with a very long encoding chain that triggers excessive CPU use and massive memory allocation during decompression.

Urllib3's ProxyManager ensures that the Proxy-Authorization header is correctly directed only to configured proxies. However, when HTTP requests bypass urllib3's proxy support, there's a risk of inadvertently setting the Proxy-Authorization header, which remains ineffective without a forwarding or tunneling proxy. Urllib3 does not recognize this header as carrying authentication data, failing to remove it during cross-origin redirects. While this scenario is uncommon and poses low risk to most users, urllib3 now proactively removes the Proxy-Authorization header during cross-origin redirects as a precautionary measure. Users are advised to utilize urllib3's proxy support or disable automatic redirects to handle the Proxy-Authorization header securely. Despite these precautions, urllib3 defaults to stripping the header to safeguard users who may inadvertently misconfigure requests.

Urllib3 1.26.4 includes a fix for CVE-2021-28363: The urllib3 library 1.26.x before 1.26.4 for Python omits SSL certificate validation in some cases involving HTTPS to HTTPS proxies. The initial connection to the HTTPS proxy (if an SSLContext isn't given via proxy_config) doesn't verify the hostname of the certificate. This means certificates for different servers that still validate properly with the default urllib3 SSLContext will be silently accepted.

Pytest-runner depends on deprecated features of setuptools and relies on features that break security mechanisms in pip. For example ‘setup_requires’ and ‘tests_require’ bypass pip --require-hashes. See also pypa/setuptools#1684.
It is recommended that you:
- Remove 'pytest-runner' from your setup_requires, preferably removing the setup_requires option.
- Remove 'pytest' and any other testing requirements from tests_require, preferably removing the tests_requires option.
- Select a tool to bootstrap and then run tests such as tox.
https://github.com/pytest-dev/pytest-runner/blob/289a77b179535d8137118e3b8591d9e727130d6d/README.rst