VAITP

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. Improper escaping in the document `SkinsCode.XWikiSkinsSheet` leads to an injection vector from view right on that document to programming rights, or in other words, it is possible to execute arbitrary script macros including Groovy and Python macros that allow remote code execution including unrestricted read and write access to all wiki contents. The attack works by opening a non-existing page with a name crafted to contain a dangerous payload. It is possible to check if an existing installation is vulnerable. See the linked GHSA for instructions on testing an installation. This issue has been patched in XWiki 14.4.8, 14.10.4 and 15.0-rc-1. Users are advised to upgrade. The fix commit `d9c88ddc` can also be applied manually to the impacted document `SkinsCode.XWikiSkinsSheet` and users unable to upgrade are advised to manually patch their installations.

Twisted is an event-based framework for internet applications, supporting Python 3.6+. Prior to version 22.4.0rc1, the Twisted Web HTTP 1.1 server, located in the `twisted.web.http` module, parsed several HTTP request constructs more leniently than permitted by RFC 7230. This non-conformant parsing can lead to desync if requests pass through multiple HTTP parsers, potentially resulting in HTTP request smuggling. Users who may be affected use Twisted Web's HTTP 1.1 server and/or proxy and also pass requests through a different HTTP server and/or proxy. The Twisted Web client is not affected. The HTTP 2.0 server uses a different parser, so it is not affected. The issue has been addressed in Twisted 22.4.0rc1. Two workarounds are available: Ensure any vulnerabilities in upstream proxies have been addressed, such as by upgrading them; or filter malformed requests by other means, such as configuration of an upstream proxy.

NLTK (Natural Language Toolkit) is a suite of open source Python modules, data sets, and tutorials supporting research and development in Natural Language Processing. Versions prior to 3.6.5 are vulnerable to regular expression denial of service (ReDoS) attacks. The vulnerability is present in PunktSentenceTokenizer, sent_tokenize and word_tokenize. Any users of this class, or these two functions, are vulnerable to the ReDoS attack. In short, a specifically crafted long input to any of these vulnerable functions will cause them to take a significant amount of execution time. If your program relies on any of the vulnerable functions for tokenizing unpredictable user input, then we would strongly recommend upgrading to a version of NLTK without the vulnerability. For users unable to upgrade the execution time can be bounded by limiting the maximum length of an input to any of the vulnerable functions. Our recommendation is to implement such a limit.

Synapse is a package for Matrix homeservers written in Python 3/Twisted. Prior to version 1.47.1, Synapse instances with the media repository enabled can be tricked into downloading a file from a remote server into an arbitrary directory. No authentication is required for the affected endpoint. The last 2 directories and file name of the path are chosen randomly by Synapse and cannot be controlled by an attacker, which limits the impact. Homeservers with the media repository disabled are unaffected. Homeservers with a federation whitelist are also unaffected, since Synapse will check the remote hostname, including the trailing `../`s, against the whitelist. Server administrators should upgrade to 1.47.1 or later. Server administrators using a reverse proxy could, at the expense of losing media functionality, may block the certain endpoints as a workaround. Alternatively, non-containerized deployments can be adapted to use the hardened systemd config.

TensorFlow is an end-to-end open source platform for machine learning. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.

GrowthBook is an open-source platform for feature flagging and A/B testing. With some self-hosted configurations in versions prior to 2022-08-29, attackers can register new accounts and upload files to arbitrary directories within the container. If the attacker uploads a Python script to the right location, they can execute arbitrary code within the container. To be affected, ALL of the following must be true: Self-hosted deployment (GrowthBook Cloud is unaffected); using local file uploads (as opposed to S3 or Google Cloud Storage); NODE_ENV set to a non-production value and JWT_SECRET set to an easily guessable string like `dev`. This issue is patched in commit 1a5edff8786d141161bf880c2fd9ccbe2850a264 (2022-08-29). As a workaround, set `JWT_SECRET` environment variable to a long random string. This will stop arbitrary file uploads, but the only way to stop attackers from registering accounts is by updating to the latest build.

In TensorFlow before 1.15.2 and 2.0.1, converting a string (from Python) to a tf.float16 value results in a segmentation fault in eager mode as the format checks for this use case are only in the graph mode. This issue can lead to denial of service in inference/training where a malicious attacker can send a data point which contains a string instead of a tf.float16 value. Similar effects can be obtained by manipulating saved models and checkpoints whereby replacing a scalar tf.float16 value with a scalar string will trigger this issue due to automatic conversions. This can be easily reproduced by tf.constant("hello", tf.float16), if eager execution is enabled. This issue is patched in TensorFlow 1.15.1 and 2.0.1 with this vulnerability patched. TensorFlow 2.1.0 was released after we fixed the issue, thus it is not affected. Users are encouraged to switch to TensorFlow 1.15.1, 2.0.1 or 2.1.0.

XWiki Platform is a generic wiki platform offering runtime services for applications built on top of it. Any user with view rights on commonly accessible documents including the menu macro can execute arbitrary Groovy, Python or Velocity code in XWiki leading to full access to the XWiki installation due to improper escaping of the macro content and parameters of the menu macro. The problem has been patched in XWiki 14.6RC1, 13.10.8 and 14.4.3. The patch (commit `2fc20891`) for the document `Menu.MenuMacro` can be manually applied or a XAR archive of a patched version can be imported. The menu macro was basically unchanged since XWiki 11.6 so on XWiki 11.6 or later the patch for version of 13.10.8 (commit `59ccca24a`) can most likely be applied, on XWiki version 14.0 and later the versions in XWiki 14.6 and 14.4.3 should be appropriate.

Snudown is a reddit-specific fork of the Sundown Markdown parser used by GitHub, with Python integration added. In affected versions snudown was found to be vulnerable to denial of service attacks to its reference table implementation. References written in markdown ` [reference_name]: https://www.example.com` are inserted into a hash table which was found to have a weak hash function, meaning that an attacker can reliably generate a large number of collisions for it. This makes the hash table vulnerable to a hash-collision DoS attack, a type of algorithmic complexity attack. Further the hash table allowed for duplicate entries resulting in long retrieval times. Proofs of concept and further discussion of the hash collision issue are discussed on the snudown GHSA(https://github.com/reddit/snudown/security/advisories/GHSA-6gvv-9q92-w5f6). Users are advised to update to version 1.7.0.