VAITP

urllib3 is a user-friendly HTTP client library for Python. urllib3 previously wouldn't remove the HTTP request body when an HTTP redirect response using status 301, 302, or 303 after the request had its method changed from one that could accept a request body (like `POST`) to `GET` as is required by HTTP RFCs. Although this behavior is not specified in the section for redirects, it can be inferred by piecing together information from different sections and we have observed the behavior in other major HTTP client implementations like curl and web browsers. Because the vulnerability requires a previously trusted service to become compromised in order to have an impact on confidentiality we believe the exploitability of this vulnerability is low. Additionally, many users aren't putting sensitive data in HTTP request bodies, if this is the case then this vulnerability isn't exploitable. Both of the following conditions must be true to be affected by this vulnerability: 1. Using urllib3 and submitting sensitive information in the HTTP request body (such as form data or JSON) and 2. The origin service is compromised and starts redirecting using 301, 302, or 303 to a malicious peer or the redirected-to service becomes compromised. This issue has been addressed in versions 1.26.18 and 2.0.7 and users are advised to update to resolve this issue. Users unable to update should disable redirects for services that aren't expecting to respond with redirects with `redirects=False` and disable automatic redirects with `redirects=False` and handle 301, 302, and 303 redirects manually by stripping the HTTP request body.

The Python "Flask-Security-Too" package is used for adding security features to your Flask application. It is an is an independently maintained version of Flask-Security based on the 3.0.0 version of Flask-Security. All versions of Flask-Security-Too allow redirects after many successful views (e.g. /login) by honoring the ?next query param. There is code in FS to validate that the url specified in the next parameter is either relative OR has the same netloc (network location) as the requesting URL. This check utilizes Pythons urlsplit library. However many browsers are very lenient on the kind of URL they accept and 'fill in the blanks' when presented with a possibly incomplete URL. As a concrete example - setting http://login?next=\\\github.com will pass FS's relative URL check however many browsers will gladly convert this to http://github.com. Thus an attacker could send such a link to an unwitting user, using a legitimate site and have it redirect to whatever site they want. This is considered a low severity due to the fact that if Werkzeug is used (which is very common with Flask applications) as the WSGI layer, it by default ALWAYS ensures that the Location header is absolute - thus making this attack vector mute. It is possible for application writers to modify this default behavior by setting the 'autocorrect_location_header=False`.

WireMock is a tool for mocking HTTP services. The proxy mode of WireMock, can be protected by the network restrictions configuration, as documented in Preventing proxying to and recording from specific target addresses. These restrictions can be configured using the domain names, and in such a case the configuration is vulnerable to the DNS rebinding attacks. A similar patch was applied in WireMock 3.0.0-beta-15 for the WireMock Webhook Extensions. The root cause of the attack is a defect in the logic which allows for a race condition triggered by a DNS server whose address expires in between the initial validation and the outbound network request that might go to a domain that was supposed to be prohibited. Control over a DNS service is required to exploit this attack, so it has high execution complexity and limited impact. This issue has been addressed in version 2.35.1 of wiremock-jre8 and wiremock-jre8-standalone, version 3.0.3 of wiremock and wiremock-standalone, version 2.6.1 of the python version of wiremock, and versions 2.35.1-1 and 3.0.3-1 of the wiremock/wiremock Docker container. Users are advised to upgrade. Users unable to upgrade should either configure firewall rules to define the list of permitted destinations or to configure WireMock to use IP addresses instead of the domain names.

There are issues with the AGE drivers for Golang and Python that enable SQL injections to occur. This impacts AGE for PostgreSQL 11 & AGE for PostgreSQL 12, all versions up-to-and-including 1.1.0, when using those drivers. The fix is to update to the latest Golang and Python drivers in addition to the latest version of AGE that is used for PostgreSQL 11 or PostgreSQL 12. The update of AGE will add a new function to enable parameterization of the cypher() function, which, in conjunction with the driver updates, will resolve this issue. Background (for those who want more information): After thoroughly researching this issue, we found that due to the nature of the cypher() function, it was not easy to parameterize the values passed into it. This enabled SQL injections, if the developer of the driver wasn't careful. The developer of the Golang and Pyton drivers didn't fully utilize parameterization, likely because of this, thus enabling SQL injections. The obvious fix to this issue is to use parameterization in the drivers for all PG SQL queries. However, parameterizing all PG queries is complicated by the fact that the cypher() function call itself cannot be parameterized directly, as it isn't a real function. At least, not the parameters that would take the graph name and cypher query. The reason the cypher() function cannot have those values parameterized is because the function is a placeholder and never actually runs. The cypher() function node, created by PG in the query tree, is transformed and replaced with a query tree for the actual cypher query during the analyze phase. The problem is that parameters - that would be passed in and that the cypher() function transform needs to be resolved - are only resolved in the execution phase, which is much later. Since the transform of the cypher() function needs to know the graph name and cypher query prior to execution, they can't be passed as parameters. The fix that we are testing right now, and are proposing to use, is to create a function that will be called prior to the execution of the cypher() function transform. This new function will allow values to be passed as parameters for the graph name and cypher query. As this command will be executed prior to the cypher() function transform, its values will be resolved. These values can then be cached for the immediately following cypher() function transform to use. As added features, the cached values will store the calling session's pid, for validation. And, the cypher() function transform will clear this cached information after function invocation, regardless of whether it was used. This method will allow the parameterizing of the cypher() function indirectly and provide a way to lock out SQL injection attacks.

pdm is a Python package and dependency manager supporting the latest PEP standards. It's possible to craft a malicious `pdm.lock` file that could allow e.g. an insider or a malicious open source project to appear to depend on a trusted PyPI project, but actually install another project. A project `foo` can be targeted by creating the project `foo-2` and uploading the file `foo-2-2.tar.gz` to pypi.org. PyPI will see this as project `foo-2` version `2`, while PDM will see this as project `foo` version `2-2`. The version must only be `parseable as a version` and the filename must be a prefix of the project name, but it's not verified to match the version being installed. Version `2-2` is also not a valid normalized version per PEP 440. Matching the project name exactly (not just prefix) would fix the issue. When installing dependencies with PDM, what's actually installed could differ from what's listed in `pyproject.toml` (including arbitrary code execution on install). It could also be used for downgrade attacks by only changing the version. This issue has been addressed in commit `6853e2642df` which is included in release version `2.9.4`. Users are advised to upgrade. There are no known workarounds for this vulnerability.

PyMdown Extensions is a set of extensions for the `Python-Markdown` markdown project. In affected versions an arbitrary file read is possible when using include file syntax. By using the syntax `--8<--"/etc/passwd"` or `--8<--"/proc/self/environ"` the content of these files will be rendered in the generated documentation. Additionally, a path relative to a specified, allowed base path can also be used to render the content of a file outside the specified base paths: `--8<-- "../../../../etc/passwd"`. Within the Snippets extension, there exists a `base_path` option but the implementation is vulnerable to Directory Traversal. The vulnerable section exists in `get_snippet_path(self, path)` lines 155 to 174 in snippets.py. Any readable file on the host where the plugin is executing may have its content exposed. This can impact any use of Snippets that exposes the use of Snippets to external users. It is never recommended to use Snippets to process user-facing, dynamic content. It is designed to process known content on the backend under the control of the host, but if someone were to accidentally enable it for user-facing content, undesired information could be exposed. This issue has been addressed in version 10.0. Users are advised to upgrade. Users unable to upgrade may restrict relative paths by filtering input.

Waitress is a Web Server Gateway Interface server for Python 2 and 3. When using Waitress versions 2.1.0 and prior behind a proxy that does not properly validate the incoming HTTP request matches the RFC7230 standard, Waitress and the frontend proxy may disagree on where one request starts and where it ends. This would allow requests to be smuggled via the front-end proxy to waitress and later behavior. There are two classes of vulnerability that may lead to request smuggling that are addressed by this advisory: The use of Python's `int()` to parse strings into integers, leading to `+10` to be parsed as `10`, or `0x01` to be parsed as `1`, where as the standard specifies that the string should contain only digits or hex digits; and Waitress does not support chunk extensions, however it was discarding them without validating that they did not contain illegal characters. This vulnerability has been patched in Waitress 2.1.1. A workaround is available. When deploying a proxy in front of waitress, turning on any and all functionality to make sure that the request matches the RFC7230 standard. Certain proxy servers may not have this functionality though and users are encouraged to upgrade to the latest version of waitress instead.

authentik is an open-source Identity Provider. Prior to versions 2023.4.3 and 2023.5.5, authentik does not verify the source of the X-Forwarded-For and X-Real-IP headers, both in the Python code and the go code. Only authentik setups that are directly accessible by users without a reverse proxy are susceptible to this. Possible spoofing of IP addresses in logs, downstream applications proxied by (built in) outpost, IP bypassing in custom flows if used. This poses a possible security risk when someone has flows or policies that check the user's IP address, e.g. when they want to ignore the user's 2 factor authentication when the user is connected to the company network. A second security risk is that the IP addresses in the logfiles and user sessions are not reliable anymore. Anybody can spoof this address and one cannot verify that the user has logged in from the IP address that is in their account's log. A third risk is that this header is passed on to the proxied application behind an outpost. The application may do any kind of verification, logging, blocking or rate limiting based on the IP address, and this IP address can be overridden by anybody that want to. Versions 2023.4.3 and 2023.5.5 contain a patch for this issue.

XWiki Platform Applications Tag and XWiki Platform Tag UI are tag applications for XWiki, a generic wiki platform. Starting with version 1.7 in XWiki Platform Applications Tag and prior to 13.10.6 and 14.4 in XWiki Platform Tag UI, the tags document `Main.Tags` in XWiki didn't sanitize user inputs properly. This allowed users with view rights on the document (default in a public wiki or for authenticated users on private wikis) to execute arbitrary Groovy, Python and Velocity code with programming rights. This also allowed bypassing all rights checks and thus both modification and disclosure of all content stored in the XWiki installation. The vulnerability could be used to impact the availability of the wiki. On XWiki versions before 13.10.4 and 14.2, this can be combined with CVE-2022-36092, meaning that no rights are required to perform the attack. The vulnerability has been patched in versions 13.10.6 and 14.4. As a workaround, the patch that fixes the issue can be manually applied to the document `Main.Tags` or the updated version of that document can be imported from version 14.4 of xwiki-platform-tag-ui using the import feature in the administration UI on XWiki 10.9 and later.

Pydantic is a data validation and settings management using Python type hinting. In affected versions passing either `'infinity'`, `'inf'` or `float('inf')` (or their negatives) to `datetime` or `date` fields causes validation to run forever with 100% CPU usage (on one CPU). Pydantic has been patched with fixes available in the following versions: v1.8.2, v1.7.4, v1.6.2. All these versions are available on pypi(https://pypi.org/project/pydantic/#history), and will be available on conda-forge(https://anaconda.org/conda-forge/pydantic) soon. See the changelog(https://pydantic-docs.helpmanual.io/) for details. If you absolutely can't upgrade, you can work around this risk using a validator(https://pydantic-docs.helpmanual.io/usage/validators/) to catch these values. This is not an ideal solution (in particular you'll need a slightly different function for datetimes), instead of a hack like this you should upgrade pydantic. If you are not using v1.8.x, v1.7.x or v1.6.x and are unable to upgrade to a fixed version of pydantic, please create an issue at https://github.com/samuelcolvin/pydantic/issues requesting a back-port, and we will endeavour to release a patch for earlier versions of pydantic.