TensorFlow is an open source platform for machine learning. If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a `nullptr`, which is not caught. An example can be seen in `tf.compat.v1.extract_volume_patches` by passing in quantized tensors as input `ksizes`. We have patched the issue in GitHub commit e9e95553e5411834d215e6770c81a83a3d0866ce. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In telephony service, there is a missing permission check. This could lead to local denial of service in telephone service with no additional execution privileges needed.
In media service, there is a missing permission check. This could lead to local denial of service in media service.
In npu driver, there is a memory corruption due to a use after free. This could lead to local denial of service in kernel.
In log service, there is a missing permission check. This could lead to local denial of service in log service.
In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In sensor driver, there is a possible buffer overflow due to a missing bounds check. This could lead to local denial of service in kernel.
In camera driver, there is a possible memory corruption due to improper locking. This could lead to local denial of service in kernel.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.
In TeleService, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed
In telephony service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.
In wifi service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed
In wifi service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed
In telephony service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.
In telephony service, there is a missing permission check. This could lead to local denial of service in telephone service with no additional execution privileges needed.
In FM service , there is a possible missing params check. This could lead to local denial of service in FM service .
In Music service, there is a missing permission check. This could lead to local denial of service in Music service with no additional execution privileges needed.
In wifi service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed
In dialer service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.
In telephony service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.
In log service, there is a missing permission check. This could lead to local denial of service in log service.
NVIDIA Linux distributions contain a vulnerability in TrustZone’s TEE_Malloc function, where an unchecked return value causing a null pointer dereference may lead to denial of service.
In Gallery service, there is a missing permission check. This could lead to local denial of service in Gallery service with no additional execution privileges needed.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In Music service, there is a missing permission check. This could lead to local denial of service in Music service with no additional execution privileges needed.
In thermal service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service local denial of service with no additional execution privileges.
In wifi service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed
In face detect driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In TeleService, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In contacts service, there is a missing permission check. This could lead to local denial of service in Contacts service with no additional execution privileges needed.
In soter service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.
In telephony service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.
In soter service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.
In camera driver, there is a possible memory corruption due to improper locking. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.SparseDenseCwiseMul`, an attacker can trigger denial of service via `CHECK`-fails or accesses to outside the bounds of heap allocated data. Since the implementation(https://github.com/tensorflow/tensorflow/blob/38178a2f7a681a7835bb0912702a134bfe3b4d84/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L68-L80) only validates the rank of the input arguments but no constraints between dimensions(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SparseDenseCwiseMul), an attacker can abuse them to trigger internal `CHECK` assertions (and cause program termination, denial of service) or to write to memory outside of bounds of heap allocated tensor buffers. 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.