TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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.

In checkDebuggingDisallowed of DeviceVersionFragment.java, there is a     possible way to access adb before SUW completion due to an insecure default     value. This could lead to local escalation of privilege with no additional     execution privileges needed. User interaction is not needed for     exploitation

In setInputMethodWithSubtypeIdLocked of InputMethodManagerService.java, there is a possible way to setup input methods that are not enabled due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-227207653

Improper access control vulnerability in dynamic receiver in ApkInstaller prior to SMR MAR-2022 Release allows unauthorized attackers to execute arbitrary activity without a proper permission

NVIDIA Linux kernel distributions contain a vulnerability in nvmap NVGPU_IOCTL_CHANNEL_SET_ERROR_NOTIFIER, where improper access control may lead to code execution, compromised integrity, or denial of service.

An issue was discovered on Samsung mobile devices with P(9.0) and Q(10.0) (Exynos 980, 9820, and 9830 chipsets) software. The NPU driver allows attackers to execute arbitrary code because of unintended write and read operations on memory. The Samsung ID is SVE-2020-18610 (November 2020).

In gpu_pixel_handle_buffer_liveness_update_ioctl of private/google-modules/gpu/mali_kbase/mali_kbase_core_linux.c, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

NVIDIA Linux distributions contain a vulnerability in nvmap ioctl, which allows any user with a local account to exploit a use-after-free condition, leading to code privilege escalation, loss of confidentiality and integrity, or denial of service.

there is a possible DCK won't be deleted after factory reset due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In onCreate of BluetoothPairingDialog.java, there is a possible tapjacking vector due to an insecure default value. This could lead to local escalation of privilege and untrusted devices accessing contact lists with no additional execution privileges needed. User interaction is needed for exploitation.Product: AndroidVersions: Android-8.0 Android-8.1 Android-9 Android-10 Android-11Android ID: A-155648639

In lwis_add_completion_fence of lwis_fence.c, there is a possible escalation of privilege due to type confusion. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In lwis_fence_signal of lwis_debug.c, there is a possible Use after Free due to improper locking. This could lead to local escalation of privilege from hal_camera_default SELinux label with no additional execution privileges needed. User interaction is not needed for exploitation.

In BCMFASTPATH of dhd_msgbuf.c, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In prepare_response_locked of lwis_transaction.c, there is a possible out of bounds write due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In AcvpOnMessage of avcp.cpp, there is a possible EOP due to uninitialized data. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In DevmemIntChangeSparse2 of devicemem_server.c, there is a possible arbitrary code execution due to a logic error in the code. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

In multiple locations, there is a possible way to reveal images across users data due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

Security misconfiguration of RKP in kernel prior to SMR Mar-2022 Release 1 allows a system not to be protected by RKP.

In multiple locations, there is a possible way in which policy migration code will never be executed due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In sec_media_protect of media.c, there is a possible permission bypass due to a race condition. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In PVRSRVRGXKickTA3DKM of rgxta3d.c, there is a possible arbitrary code execution due to improper input validation. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

In setSkipPrompt of AssociationRequest.java , there is a possible way to establish a companion device association without any confirmation due to CDM. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In CompanionDeviceManagerService.java, there is a possible way to pair a companion device without user acceptance due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In multiple functions of ZygoteProcess.java, there is a possible way to achieve code execution as any app via WRITE_SECURE_SETTINGS due to unsafe deserialization. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.

In pblS2mpuResume of s2mpu.c, there is a possible mitigation bypass due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In lwis_process_transactions_in_queue of lwis_transaction.c, there is a possible use after free due to a use after free. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In ppcfw_enable of ppcfw.c, there is a possible EoP due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In valid_address of syscall.c, there is a possible out of bounds read due to an incorrect bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In ppcfw_init_secpolicy of ppcfw.c, there is a possible permission bypass due to uninitialized data. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In pmucal_rae_handle_seq_int of flexpmu_cal_rae.c, there is a possible arbitrary write due to uninitialized data. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In onSkipButtonClick of FaceEnrollFoldPage.java, there is a possible way to access the file the app cannot access due to Intent Redirect GRANT_URI_PERMISSIONS Attack. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In vring_size of external/headers/include/virtio/virtio_ring.h, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In sm_mem_compat_get_vmm_obj of lib/sm/shared_mem.c, there is a possible arbitrary physical memory access due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In lwis_allocator_free of lwis_allocator.c, there is a possible memory corruption due to a use after free. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In vring_init of external/headers/include/virtio/virtio_ring.h, there is a possible out of bounds write due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In the APEX module framework of AOSP, there is a possible malicious update to platform components due to improperly used crypto. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. More details on this can be found in the referenced links.

In CreateAudioBroadcast of broadcaster.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In CreateAudioBroadcast of broadcaster.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In multiple functions of btm_ble_gap.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.

In ppmp_unprotect_buf of drm_fw.c, there is a possible compromise of protected memory due to a logic error in the code. This could lead to local escalation of privilege to TEE with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments. Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since `OP_REQUIRES`(https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function. Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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.

In checkKeyIntentParceledCorrectly of AccountManagerService.java, there is a possible way to launch arbitrary activities using system privileges due to Parcel Mismatch. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB read on heap in the TFLite implementation of `Split_V`(https://github.com/tensorflow/tensorflow/blob/c59c37e7b2d563967da813fa50fe20b21f4da683/tensorflow/lite/kernels/split_v.cc#L99). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the `SizeOfDimension` function(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/kernel_util.h#L148-L150) will access data outside the bounds of the tensor shape array. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. 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.

TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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.

TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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.

TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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.