An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. Kernel Wi-Fi drivers allow out-of-bounds Read or Write operations (e.g., a buffer overflow). The Samsung IDs are SVE-2019-16125, SVE-2019-16134, SVE-2019-16158, SVE-2019-16159, SVE-2019-16319, SVE-2019-16320, SVE-2019-16337, SVE-2019-16464, SVE-2019-16465, SVE-2019-16467 (March 2020).

In skb_headlen of /include/linux/skbuff.h, there is a possible out of bounds read due to memory corruption. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-153881554

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. 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 display, there is a possible out of bounds read due to a missing bounds check. This could lead to local escalation of privilege if a malicious actor has already obtained the System privilege. User interaction is not needed for exploitation. Patch ID: ALPS10196993; Issue ID: MSV-4805.

TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 an attacker can cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. 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 libdexfile, there is a possible out of bounds read 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.

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. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The implementation(https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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 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.

NVIDIA SHIELD TV, all versions prior to 8.2.2, contains a vulnerability in the NVDEC component, in which an attacker can read from or write to a memory location that is outside the intended boundary of the buffer, which may lead to denial of service or escalation of privileges.

In xhci_vendor_get_ops of xhci.c, there is a possible out of bounds read 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.Product: AndroidVersions: Android kernelAndroid ID: A-194461020References: Upstream kernel

In V4L2SliceVideoDecodeAccelerator::Dequeue of v4l2_slice_video_decode_accelerator.cc, there is a possible out of bounds read of a function pointer 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. Product: Android. Versions: Android-8.1 Android-9. Android ID: A-112181526.

In the Mediatek Preloader, there are out of bounds reads and writes due to an exposed interface that allows arbitrary peripheral memory mapping with insufficient blacklisting/whitelisting. This could lead to local elevation of privilege, given physical access to the device with no additional execution privileges needed. User interaction is needed for exploitation.

In affected versions of TensorFlow the tf.raw_ops.DataFormatVecPermute API does not validate the src_format and dst_format attributes. The code assumes that these two arguments define a permutation of NHWC. This can result in uninitialized memory accesses, read outside of bounds and even crashes. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

In validateNode of ResourceTypes.cpp, 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 aoc_service_read_message of aoc_ipc_core.c, there is a possible out of bounds read due to improper input validation. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.

In multiple locations, there is a possible way to read protected files 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.

Out-of-Bounds Read in Virglrenderer in ChromeOS 16093.57.0 allows a malicious guest VM to achieve arbitrary address access within the crosvm sandboxed process, potentially leading to VM escape via crafted vertex elements data triggering an out-of-bounds read in util_format_description.

In tmu_tz_control of tmu.c, there is a possible out of bounds read 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.

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.

In DMService, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges.

In loadDataAndPostValue of multiple files, there is a possible way to obscure permission usage 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.

Google gVisor's runsc component exhibited a local privilege escalation vulnerability due to incorrect handling of file access permissions, which allowed unprivileged users to access restricted files. This occurred because the process initially ran with root-like permissions until the first fork.

In multiple functions of AccountManager.java, there is a possible permissions bypass. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-145206842

In multiple functions of CameraService.cpp, there is a possible way to use the camera from the background due to a permissions bypass. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In getDestinationForApp of SpaAppBridgeActivity, there is a possible cross-user file reveal 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 multiple functions of LocationProviderManager.java, there is a possible background activity launch 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 Connectivity Service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges.

In validateUriSchemeAndPermission of DisclaimersParserImpl.java , there is a possible way to access data from another user due to a confused deputy. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In onHandleForceStop of VoiceInteractionManagerService.java, there is a bug that could cause the system to incorrectly revert to the default assistant application when a user-selected assistant is forcibly stopped due to a logic error in the code. This could lead to local escalation of privilege where the default assistant app is automatically granted ROLE_ASSISTANT with no additional execution privileges needed. User interaction is not needed for exploitation.

In getComponentName of AccessibilitySettingsUtils.java, there is a possible way to for a malicious Talkback service to be enabled instead of the system component 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 AccessibilityServiceConnection.java, there is a possible background activity launch 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.

An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. The S.LSI Wi-Fi drivers have a buffer overflow. The Samsung ID is SVE-2020-16906 (May 2020).

A local attacker, as a different local user, may be able to send a HTTP request to 127.0.0.1:10000 after the user (typically a developer) manually invoked the ./tools/run-dev-server script. It is recommended to upgrade to any version beyond 24.2

An issue was discovered on LG mobile devices with Android OS 8.0, 8.1, 9.0, and 10.0 (MTK chipsets) software. The MTK kernel does not properly implement exception handling, allowing an attacker to gain privileges. The LG ID is LVE-SMP-200001 (February 2020).

In vowifiservice, there is a possible missing permission check.This could lead to local escalation of privilege with no additional execution privileges

In Connectivity Service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges.

In telephony service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges.

In vowifiservice, there is a possible missing permission check.This could lead to local escalation of privilege with no additional execution privileges

In DMService, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges.

In telephony service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges.

Access of memory location after end of buffer for some Intel Unison software may allow an authenticated user to potentially enable escalation of privilege via local access.

In vowifiservice, there is a possible missing permission check.This could lead to local escalation of privilege with no additional execution privileges

An issue was discovered on Samsung mobile devices with O(8.0), P(9.0), and Q(10.0) (Broadcom chipsets) software. A kernel driver heap overflow leads to arbitrary code execution. The Samsung ID is SVE-2019-15880 (March 2020).

An issue was discovered on Samsung mobile devices with P(9.0) and Q(10.0) software. PROCA allows a use-after-free and arbitrary code execution. The Samsung ID is SVE-2019-16132 (February 2020).

An issue was discovered on Samsung mobile devices with P(9.0) and Q(10.0) (Exynos 9610 chipsets) software. There is an arbitrary kfree in the vipx and vertex drivers. The Samsung ID is SVE-2019-16294 (February 2020).

An issue was discovered on Samsung mobile devices with P(9.0) and Q(10.0) software. There is a stack overflow in the kperfmon driver. The Samsung ID is SVE-2019-15876 (January 2020).

In vowifiservice, there is a possible missing permission check.This could lead to local escalation of privilege with no additional execution privileges