#dos

Actors claiming to be the defunct ransomware group are targeting one of Akami’s customers with a Layer 7 attack, demanding an extortion payment in Bitcoin.

### Impact PocketMine-MP caps maximum chat message length at 512 Unicode characters, or about 2048 bytes. No more than 2 chat messages may be sent per tick. However, due to legacy reasons, incoming chat message blobs are split by `\n`, and each part is treated as a separate message, the length of each part is individually checked. The length of the whole message is not checked. This leads to an exploitable performance issue, in which a malicious client may send a chat packet of several megabytes containing nothing but `\n` newline characters. The server will parse this into a very large array and spend a long time (several milliseconds) iterating over it for no reason. Furthermore, due to the lack of sufficient rate limit checks before parsing messages, malicious clients may bombard the server with many thousands of these malicious messages, causing lockups for a significant amount of time (seconds or minutes). ### Patches This bug was addressed in https://github.com/pmmp/PocketMine...

Supply chain and ransomware attacks increased dramatically in 2021, which explains why so many data breaches in Verizon's "2022 Data Breach Investigations Report" were grouped as system intrusion.

MariaDB Server before 10.7 is vulnerable to Denial of Service. In extra/mariabackup/ds_compress.cc, when an error occurs (i.e., going to the err label) while executing the method create_worker_threads, the held lock thd->ctrl_mutex is not released correctly, which allows local users to trigger a denial of service due to the deadlock.

MariaDB Server before 10.7 is vulnerable to Denial of Service. In extra/mariabackup/ds_compress.cc, when an error occurs (pthread_create returns a nonzero value) while executing the method create_worker_threads, the held lock is not released correctly, which allows local users to trigger a denial of service due to the deadlock.

MariaDB Server before 10.7 is vulnerable to Denial of Service. In extra/mariabackup/ds_xbstream.cc, when an error occurs (stream_ctxt->dest_file == NULL) while executing the method xbstream_open, the held lock is not released correctly, which allows local users to trigger a denial of service due to the deadlock.

MariaDB Server before 10.7 is vulnerable to Denial of Service. While executing the plugin/server_audit/server_audit.c method log_statement_ex, the held lock lock_bigbuffer is not released correctly, which allows local users to trigger a denial of service due to the deadlock.

A denial of service vulnerability exists in the OAS Engine SecureConfigValues functionality of Open Automation Software OAS Platform V16.00.0112. A specially-crafted network request can lead to loss of communications. An attacker can send a network request to trigger this vulnerability.

In libjpeg before 1.64, BitStream<false>::Get in bitstream.hpp has an assertion failure that may cause denial of service. This is related to out-of-bounds array access during arithmetically coded lossless scan or arithmetically coded sequential scan.

### Impact The implementation of depthwise ops in TensorFlow is vulnerable to a denial of service via `CHECK`-failure (assertion failure) caused by overflowing the number of elements in a tensor: ```python import tensorflow as tf input = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32) filter_sizes = tf.constant(1879048192, shape=[13], dtype=tf.int32) out_backprop = tf.constant(1, shape=[1, 4, 4, 3], dtype=tf.float32) tf.raw_ops.DepthwiseConv2dNativeBackpropFilter( input=input, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 1, 1, 1], padding="SAME") ``` This is another instance of [TFSA-2021-198](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/security/advisory/tfsa-2021-198.md) (CVE-2021-41197). ### Patches We have patched the issue in GitHub commit [3796cc4fcd93ae55812a457abc96dcd55fbb854b](https://github.com/tensorflow/tensorflow/commit/3796cc4fcd93ae55812a457abc96dcd55fbb854b). The fix will be included in TensorFlow 2.9.0. We will...