VAITP

cdo-local-uuid project provides a specialized UUID-generating function that can, on user request, cause a program to generate deterministic UUIDs. An information leakage vulnerability is present in `cdo-local-uuid` at version `0.4.0`, and in `case-utils` in unpatched versions (matching the pattern `0.x.0`) at and since `0.5.0`, before `0.15.0`. The vulnerability stems from a Python function, `cdo_local_uuid.local_uuid()`, and its original implementation `case_utils.local_uuid()`.

cryptography is a package designed to expose cryptographic primitives and recipes to Python developers. Calling `load_pem_pkcs7_certificates` or `load_der_pkcs7_certificates` could lead to a NULL-pointer dereference and segfault. Exploitation of this vulnerability poses a serious risk of Denial of Service (DoS) for any application attempting to deserialize a PKCS7 blob/certificate. The consequences extend to potential disruptions in system availability and stability. This vulnerability has been patched in version 41.0.6.

Gradio is an open-source Python package that allows you to quickly build a demo or web application for your machine learning model, API, or any arbitary Python function. Versions of `gradio` prior to 4.11.0 contained a vulnerability in the `/file` route which made them susceptible to file traversal attacks in which an attacker could access arbitrary files on a machine running a Gradio app with a public URL (e.g. if the demo was created with `share=True`, or on Hugging Face Spaces) if they knew the path of files to look for. This issue has been patched in version 4.11.0.

SAP BTP Security Services Integration Library ([Python] sap-xssec) - versions < 4.1.0, allow under certain conditions an escalation of privileges. On successful exploitation, an unauthenticated attacker can obtain arbitrary permissions within the application.

An issue was found in CPython 3.12.0 `subprocess` module on POSIX platforms. The issue was fixed in CPython 3.12.1 and does not affect other stable releases. When using the `extra_groups=` parameter with an empty list as a value (ie `extra_groups=[]`) the logic regressed to not call `setgroups(0, NULL)` before calling `exec()`, thus not dropping the original processes' groups before starting the new process. There is no issue when the parameter isn't used or when any value is used besides an empty list. This issue only impacts CPython processes run with sufficient privilege to make the `setgroups` system call (typically `root`).

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

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.