In onCreate of WifiDialogActivity.java, there is a possible way to bypass the DISALLOW_ADD_WIFI_CONFIG restriction 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.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of SVDF in TFLite is [vulnerable to a null pointer error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/svdf.cc#L300-L313). The [`GetVariableInput` function](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L115-L119) can return a null pointer but `GetTensorData` assumes that the argument is always a valid tensor. Furthermore, because `GetVariableInput` calls [`GetMutableInput`](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L82-L90) which might return `nullptr`, the `tensor->is_variable` expression can also trigger a null pointer exception. We have patched the issue in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76. 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 DevmemXIntUnreserveRange 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.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. 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 the code for `tf.raw_ops.SaveV2` does not properly validate the inputs and an attacker can trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses `ValidateInputs` to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses `OP_REQUIRES` which translates to setting the `Status` object of the current `OpKernelContext` to an error status, followed by an empty `return` statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in `Compute` that follows the call to `ValidateInputs`. This is equivalent to lacking the validation. We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986. 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 an attacker can cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.Map*` and `tf.raw_ops.OrderedMap*` operations. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L222-L248) has a check in place to ensure that `indices` is in ascending order, but does not check that `indices` is not empty. We have patched the issue in GitHub commit 532f5c5a547126c634fefd43bbad1dc6417678ac. 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 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.
Race condition in the ip4_datagram_release_cb function in net/ipv4/datagram.c in the Linux kernel before 3.15.2 allows local users to gain privileges or cause a denial of service (use-after-free) by leveraging incorrect expectations about locking during multithreaded access to internal data structures for IPv4 UDP sockets.
In RGXFWChangeOSidPriority of rgxfwutils.c, there is a possible arbitrary code execution due to a missing bounds check. This could lead to local escalation of privilege in the kernel 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. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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 fixUpIncomingShortcutInfo of ShortcutService.java, there is a possible way to view another user's image 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 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.
In installExistingPackageAsUser of InstallPackageHelper.java, there is a possible carrier restriction 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 adjustStreamVolume of AudioService.java, there is a possible way for unprivileged app to change audio stream volume 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.Product: AndroidVersions: Android-12Android ID: A-189857506
An attacker can change the pointer to untrusted memory to point to trusted memory region which causes copying trusted memory to trusted memory, if the latter is later copied out, it allows for reading of memory regions from the trusted region. It is recommended to update past 0.6.2 or git commit https://github.com/google/asylo/commit/53ed5d8fd8118ced1466e509606dd2f473707a5c
An attacker can craft a specific IdaPro *.i64 file that will cause the BinDiff plugin to load an invalid memory offset. This can allow the attacker to control the instruction pointer and execute arbitrary code. It is recommended to upgrade BinDiff 7
The Security Team discovered an integer overflow bug that allows an attacker with code execution to issue memory cache invalidation operations on pages that they don’t own, allowing them to control kernel memory from userspace. We recommend upgrading to kernel version 4.1 or beyond.
arch/x86/kernel/entry_64.S in the Linux kernel before 3.17.5 does not properly handle faults associated with the Stack Segment (SS) segment register, which allows local users to gain privileges by triggering an IRET instruction that leads to access to a GS Base address from the wrong space.
In IoT Devices SDK, there is an implementation of calloc() that doesn't have a length check. An attacker could pass in memory objects larger than the buffer and wrap around to have a smaller buffer than required, allowing the attacker access to the other parts of the heap. We recommend upgrading the Google Cloud IoT Device SDK for Embedded C used to 1.0.3 or greater.
In multiple files, there is a possible way to capture the device screen when disallowed by device policy 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 wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
An attacker can modify the address to point to trusted memory to overwrite arbitrary trusted memory. It is recommended to update past 0.6.2 or git commit https://github.com/google/asylo/commit/53ed5d8fd8118ced1466e509606dd2f473707a5c
In ion service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In power manager, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In telecom service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In telecom service, there is a possible way to write permission usage records of an app due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In telecom service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In camera service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In engineermode service, there is a possible way to write permission usage records of an app due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In telocom service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In gpu_pixel_handle_buffer_liveness_update_ioctl of private/google-modules/gpu/mali_kbase/platform/pixel/pixel_gpu_slc.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 wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In telecom service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In wifi service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed
In onSomePackagesChanged of VoiceInteractionManagerService.java, there is a possible way for a third party application's component name to persist even after uninstalling 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 NotificationStation.java, there is a possible cross-profile information disclosure 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 multiple locations, there is a possible way to record audio via a background app 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 mem_protect.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.
In __pkvm_load_tracing of trace.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 checkPermissions of SafeActivityOptions.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 validateIconUserBoundary of PrintManagerService.java, there is a possible cross-user image leak 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 init_pkvm_hyp_vcpu of pkvm.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 onNullBinding of RemoteFillService.java, there is a possible background activity launch 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 phasechecksercer, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed