There is a race condition vulnerability in the binder driver subsystem in the kernel.Successful exploitation of this vulnerability may affect kernel stability.

In affected Octopus Server versions when the server HTTP and HTTPS bindings are configured to localhost, Octopus Server will allow open redirects.

Tensorflow is an Open Source Machine Learning Framework. The implementation of `QuantizedMaxPool` has an undefined behavior where user controlled inputs can trigger a reference binding to null pointer. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

treq is an HTTP library inspired by requests but written on top of Twisted's Agents. Treq's request methods (`treq.get`, `treq.post`, etc.) and `treq.client.HTTPClient` constructor accept cookies as a dictionary. Such cookies are not bound to a single domain, and are therefore sent to *every* domain ("supercookies"). This can potentially cause sensitive information to leak upon an HTTP redirect to a different domain., e.g. should `https://example.com` redirect to `http://cloudstorageprovider.com` the latter will receive the cookie `session`. Treq 2021.1.0 and later bind cookies given to request methods (`treq.request`, `treq.get`, `HTTPClient.request`, `HTTPClient.get`, etc.) to the origin of the *url* parameter. Users are advised to upgrade. For users unable to upgrade Instead of passing a dictionary as the *cookies* argument, pass a `http.cookiejar.CookieJar` instance with properly domain- and scheme-scoped cookies in it.

Micronaut is a JVM-based, full stack Java framework designed for building JVM web applications with support for Java, Kotlin and the Groovy language. In affected versions sending an invalid Content Type header leads to memory leak in DefaultArgumentConversionContext as this type is erroneously used in static state. ### Impact Sending an invalid Content Type header leads to memory leak in `DefaultArgumentConversionContext` as this type is erroneously used in static state. ### Patches The problem is patched in Micronaut 3.2.7 and above. ### Workarounds The default content type binder can be replaced in an existing Micronaut application to mitigate the issue: ```java package example; import java.util.List; import io.micronaut.context.annotation.Replaces; import io.micronaut.core.convert.ConversionService; import io.micronaut.http.MediaType; import io.micronaut.http.bind.DefaultRequestBinderRegistry; import io.micronaut.http.bind.binders.RequestArgumentBinder; import jakarta.inject.Singleton; @Singleton @Replaces(DefaultRequestBinderRegistry.class) class FixedRequestBinderRegistry extends DefaultRequestBinderRegistry { public FixedRequestBinderRegistry(ConversionService conversionService, List<RequestArgumentBinder> binders) { super(conversionService, binders); } @Override protected void registerDefaultConverters(ConversionService<?> conversionService) { super.registerDefaultConverters(conversionService); conversionService.addConverter(CharSequence.class, MediaType.class, charSequence -> { try { return MediaType.of(charSequence); } catch (IllegalArgumentException e) { return null; } }); } } ``` ### References Commit that introduced the vulnerability https://github.com/micronaut-projects/micronaut-core/commit/b8ec32c311689667c69ae7d9f9c3b3a8abc96fe3 ### For more information If you have any questions or comments about this advisory: * Open an issue in [Micronaut Core](https://github.com/micronaut-projects/micronaut-core/issues) * Email us at [info@micronaut.io](mailto:info@micronaut.io)

Jenkins Credentials Binding Plugin 1.27 and earlier does not perform a permission check in a method implementing form validation, allowing attackers with Overall/Read access to validate if a credential ID refers to a secret file credential and whether it's a zip file.

Windows Bind Filter Driver Elevation of Privilege Vulnerability

containerd is an open source container runtime. On installations using SELinux, such as EL8 (CentOS, RHEL), Fedora, or SUSE MicroOS, with containerd since v1.5.0-beta.0 as the backing container runtime interface (CRI), an unprivileged pod scheduled to the node may bind mount, via hostPath volume, any privileged, regular file on disk for complete read/write access (sans delete). Such is achieved by placing the in-container location of the hostPath volume mount at either `/etc/hosts`, `/etc/hostname`, or `/etc/resolv.conf`. These locations are being relabeled indiscriminately to match the container process-label which effectively elevates permissions for savvy containers that would not normally be able to access privileged host files. This issue has been resolved in version 1.5.9. Users are advised to upgrade as soon as possible.

In code generated by BuildParcelFields of generate_cpp.cpp, there is a possible way for a crafted parcelable to reveal uninitialized memory of a target process due to uninitialized data. This could lead to local information disclosure across Binder transactions with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12Android ID: A-198346478

TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` has an undefined behavior due to binding a reference to `nullptr`. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

Grafana is an open-source platform for monitoring and observability. In affected versions if an attacker is able to convince a victim to visit a URL referencing a vulnerable page, arbitrary JavaScript content may be executed within the context of the victim's browser. The user visiting the malicious link must be unauthenticated and the link must be for a page that contains the login button in the menu bar. The url has to be crafted to exploit AngularJS rendering and contain the interpolation binding for AngularJS expressions. AngularJS uses double curly braces for interpolation binding: {{ }} ex: {{constructor.constructor(‘alert(1)’)()}}. When the user follows the link and the page renders, the login button will contain the original link with a query parameter to force a redirect to the login page. The URL is not validated and the AngularJS rendering engine will execute the JavaScript expression contained in the URL. Users are advised to upgrade as soon as possible. If for some reason you cannot upgrade, you can use a reverse proxy or similar to block access to block the literal string {{ in the path.

In BIND 9.3.0 -> 9.11.35, 9.12.0 -> 9.16.21, and versions 9.9.3-S1 -> 9.11.35-S1 and 9.16.8-S1 -> 9.16.21-S1 of BIND Supported Preview Edition, as well as release versions 9.17.0 -> 9.17.18 of the BIND 9.17 development branch, exploitation of broken authoritative servers using a flaw in response processing can cause degradation in BIND resolver performance. The way the lame cache is currently designed makes it possible for its internal data structures to grow almost infinitely, which may cause significant delays in client query processing.

Windows Bind Filter Driver Information Disclosure Vulnerability

Windows Bind Filter Driver Elevation of Privilege Vulnerability

A Cross-site scripting (XSS) vulnerability in Users in Qiong ICP EyouCMS 1.5.4 allows remote attackers to inject arbitrary web script or HTML via the `title` parameter in bind_email function.

BinderHub is a kubernetes-based cloud service that allows users to share reproducible interactive computing environments from code repositories. In affected versions a remote code execution vulnerability has been identified in BinderHub, where providing BinderHub with maliciously crafted input could execute code in the BinderHub context, with the potential to egress credentials of the BinderHub deployment, including JupyterHub API tokens, kubernetes service accounts, and docker registry credentials. This may provide the ability to manipulate images and other user created pods in the deployment, with the potential to escalate to the host depending on the underlying kubernetes configuration. Users are advised to update to version 0.2.0-n653. If users are unable to update they may disable the git repo provider by specifying the `BinderHub.repo_providers` as a workaround.

In BIND 9.16.19, 9.17.16. Also, version 9.16.19-S1 of BIND Supported Preview Edition When a vulnerable version of named receives a query under the circumstances described above, the named process will terminate due to a failed assertion check. The vulnerability affects only BIND 9 releases 9.16.19, 9.17.16, and release 9.16.19-S1 of the BIND Supported Preview Edition.

If remote logging is not used, the worker (in the case of CeleryExecutor) or the scheduler (in the case of LocalExecutor) runs a Flask logging server and is listening on a specific port and also binds on 0.0.0.0 by default. This logging server had no authentication and allows reading log files of DAG jobs. This issue affects Apache Airflow < 2.1.2.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.