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Untrusted search path vulnerability in AttacheCase ver.3.6.1.0 and earlier allows an attacker to gain privileges and execute arbitrary code via a Trojan horse DLL in an unspecified directory.

An issue was discovered in the friendlycaptcha_official (aka Integration of Friendly Captcha) extension before 0.1.4 for TYPO3. The extension fails to check the requirement of the captcha field in submitted form data, allowing a remote user to bypass the captcha check. This only affects the captcha integration for the ext:form extension.

A flaw was found in the REST API in StarWind Stack. REST command, which manipulates a virtual disk, doesn’t check input parameters. Some of them go directly to bash as part of a script. An attacker with non-root user access can inject arbitrary data into the command that will be executed with root privileges. This affects StarWind SAN and NAS v0.2 build 1633.

### Impact If `QuantizeAndDequantizeV3` is given a nonscalar `num_bits` input tensor, it results in a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf signed_input = True range_given = False narrow_range = False axis = -1 input = tf.constant(-3.5, shape=[1], dtype=tf.float32) input_min = tf.constant(-3.5, shape=[1], dtype=tf.float32) input_max = tf.constant(-3.5, shape=[1], dtype=tf.float32) num_bits = tf.constant([], shape=[0], dtype=tf.int32) tf.raw_ops.QuantizeAndDequantizeV3(input=input, input_min=input_min, input_max=input_max, num_bits=num_bits, signed_input=signed_input, range_given=range_given, narrow_range=narrow_range, axis=axis) ``` ### Patches We have patched the issue in GitHub commit [f3f9cb38ecfe5a8a703f2c4a8fead434ef291713](https://github.com/tensorflow/tensorflow/commit/f3f9cb38ecfe5a8a703f2c4a8fead434ef291713). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1...

### Impact The implementation of `FractionalAvgPoolGrad` does not fully validate the input `orig_input_tensor_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack. ```python import tensorflow as tf overlapping = True orig_input_tensor_shape = tf.constant(-1879048192, shape=[4], dtype=tf.int64) out_backprop = tf.constant([], shape=[0,0,0,0], dtype=tf.float64) row_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64) col_pooling_sequence = tf.constant(1, shape=[4], dtype=tf.int64) tf.raw_ops.FractionalAvgPoolGrad(orig_input_tensor_shape=orig_input_tensor_shape, out_backprop=out_backprop, row_pooling_sequence=row_pooling_sequence, col_pooling_sequence=col_pooling_sequence, overlapping=overlapping) ``` ### Patches We have patched the issue in GitHub commit [03a659d7be9a1154fdf5eeac221e5950fec07dad](https://github.com/tensorflow/tensorflow/commit/03a659d7be9a1154fdf5eeac221e5950fec07dad). The fix will be inc...

### Impact The implementation of `AvgPool3DGradOp` does not fully validate the input `orig_input_shape`. This results in an overflow that results in a `CHECK` failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf ksize = [1, 1, 1, 1, 1] strides = [1, 1, 1, 1, 1] padding = "SAME" data_format = "NDHWC" orig_input_shape = tf.constant(1879048192, shape=[5], dtype=tf.int32) grad = tf.constant(1, shape=[1,3,2,4,2], dtype=tf.float32) tf.raw_ops.AvgPool3DGrad(orig_input_shape=orig_input_shape, grad=grad, ksize=ksize, strides=strides, padding=padding, data_format=data_format) ``` ### Patches We have patched the issue in GitHub commit [9178ac9d6389bdc54638ab913ea0e419234d14eb](https://github.com/tensorflow/tensorflow/commit/9178ac9d6389bdc54638ab913ea0e419234d14eb). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in ...

### Impact If `FakeQuantWithMinMaxVars` is given `min` or `max` tensors of a nonzero rank, it results in a `CHECK` fail that can be used to trigger a denial of service attack. ```python import tensorflow as tf num_bits = 8 narrow_range = False inputs = tf.constant(0, shape=[2,3], dtype=tf.float32) min = tf.constant(0, shape=[2,3], dtype=tf.float32) max = tf.constant(0, shape=[2,3], dtype=tf.float32) tf.raw_ops.FakeQuantWithMinMaxVars(inputs=inputs, min=min, max=max, num_bits=num_bits, narrow_range=narrow_range) ``` ### Patches We have patched the issue in GitHub commit [785d67a78a1d533759fcd2f5e8d6ef778de849e0](https://github.com/tensorflow/tensorflow/commit/785d67a78a1d533759fcd2f5e8d6ef778de849e0). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tens...

### Impact When `tf.random.gamma` receives large input shape and rates, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf arg_0=tf.random.uniform(shape=(4,), dtype=tf.int32, maxval=65536) arg_1=tf.random.uniform(shape=(4, 4), dtype=tf.float64, maxval=None) arg_2=tf.random.uniform(shape=(4, 4, 4, 4, 4), dtype=tf.float64, maxval=None) arg_3=tf.float64 arg_4=48 arg_5='None' tf.random.gamma(shape=arg_0, alpha=arg_1, beta=arg_2, dtype=arg_3, seed=arg_4, name=arg_5) ``` ### Patches We have patched the issue in GitHub commit [552bfced6ce4809db5f3ca305f60ff80dd40c5a3](https://github.com/tensorflow/tensorflow/commit/552bfced6ce4809db5f3ca305f60ff80dd40c5a3). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For more information Please consult [our security guide](https://github.com/tens...

### Impact When `Unbatch` receives a nonscalar input `id`, it gives a `CHECK` fail that can trigger a denial of service attack. ```python import tensorflow as tf import numpy as np arg_0=tf.constant(value=np.random.random(size=(3, 3, 1)), dtype=tf.float64) arg_1=tf.constant(value=np.random.randint(0,100,size=(3, 3, 1)), dtype=tf.int64) arg_2=tf.constant(value=np.random.randint(0,100,size=(3, 3, 1)), dtype=tf.int64) arg_3=47 arg_4='' arg_5='' tf.raw_ops.Unbatch(batched_tensor=arg_0, batch_index=arg_1, id=arg_2, timeout_micros=arg_3, container=arg_4, shared_name=arg_5) ``` ### Patches We have patched the issue in GitHub commit [4419d10d576adefa36b0e0a9425d2569f7c0189f](https://github.com/tensorflow/tensorflow/commit/4419d10d576adefa36b0e0a9425d2569f7c0189f). The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. ### For...

The ahci_commit_buf function in ide/ahci.c in QEMU allows attackers to cause a denial of service (NULL dereference) when the command header 'ad->cur_cmd' is null.