NVIDIA Windows GPU Display Driver, all versions, contains a vulnerability in multiple components in which a securely loaded system DLL will load its dependencies in an insecure fashion, which may lead to code execution or denial of service.

NVIDIA Virtual GPU Manager contains a vulnerability in the vGPU plugin, in which the software reads from a buffer by using buffer access mechanisms such as indexes or pointers that reference memory locations after the targeted buffer, which may lead to code execution, denial of service, escalation of privileges, or information disclosure. This affects vGPU version 8.x (prior to 8.4), version 9.x (prior to 9.4) and version 10.x (prior to 10.3).

NVIDIA Virtual GPU Manager contains a vulnerability in the vGPU plugin in which it may have the use-after-free vulnerability while freeing some resources, which may lead to denial of service, code execution, and information disclosure. This affects vGPU version 8.x (prior to 8.5), version 10.x (prior to 10.4) and version 11.0.

NVIDIA Windows GPU Display Driver, all versions, contains a vulnerability in the NVIDIA Control Panel component in which a user is presented with a dialog box for input by a high-privilege process, which may lead to escalation of privileges.

NVIDIA JetPack SDK, version 4.2 and 4.3, contains a vulnerability in its installation scripts in which permissions are incorrectly set on certain directories, which can lead to escalation of privileges.

NVIDIA Windows GPU Display Driver, all versions, contains a vulnerability in the NVIDIA Control Panel component, in which an attacker with local system access can corrupt a system file, which may lead to denial of service or escalation of privileges.

NVIDIA Windows GPU Display Driver, all versions, contains a vulnerability in the service host component, in which the application resources integrity check may be missed. Such an attack may lead to code execution, denial of service or information disclosure.

In NVIDIA Jetson TX1 L4T R32 version branch prior to R32.2, Tegra bootloader contains a vulnerability in nvtboot in which the nvtboot-cpu image is loaded without the load address first being validated, which may lead to code execution, denial of service, or escalation of privileges.

NVIDIA GeForce Experience versions prior to 3.19 contains a vulnerability in the Web Helper component, in which an attacker with local system access can craft input that may not be properly validated. Such an attack may lead to code execution, denial of service or information disclosure.

NVIDIA GeForce Experience, all versions prior to 3.20.1, contains a vulnerability in the Downloader component in which a user with local system access can craft input that may allow malicious files to be downloaded and saved. This behavior may lead to code execution, denial of service, or information disclosure.

A Memory Corruption Vulnerability exists in NVIDIA Graphics Drivers 29549 due to an unknown function in the file proc/driver/nvidia/registry.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape, where an unprivileged regular user can access administrator- privileged registers, which may lead to denial of service, information disclosure, and data tampering.

NVIDIA Jetson Linux Driver Package contains a vulnerability in the Cboot module tegrabl_cbo.c, where insufficient validation of untrusted data may allow a local attacker with elevated privileges to cause a memory buffer overflow, which may lead to code execution, loss of integrity, limited denial of service, and some impact to confidentiality.

NVIDIA GeForce Experience contains a vulnerability in all versions prior to 3.16 on Windows in which an attacker who has access to a local user account can plant a malicious dynamic link library (DLL) during application installation, which may lead to escalation of privileges.

NVIDIA Jetson TX2 contains a vulnerability in the kernel driver where input/output control (IOCTL) handling for user mode requests could create a non-trusted pointer dereference, which may lead to information disclosure, denial of service, escalation of privileges, or code execution. The updates apply to all versions prior to R28.3.

NVIDIA GeForce Experience contains a vulnerability in all versions prior to 3.16 during application installation on Windows 7 in elevated privilege mode, where a local user who initiates a browser session may obtain escalation of privileges on the browser.

NVIDIA distributions of Jetson Linux contain a vulnerability where an error in the IOMMU configuration may allow an unprivileged attacker with physical access to the board direct read/write access to the entire system address space through the PCI bus. Such an attack could result in denial of service, code execution, escalation of privileges, and impact to data integrity and confidentiality. The scope impact may extend to other components.

NVIDIA Windows GPU Display Driver contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape where a value passed from a user to the driver is not correctly validated and used as the index to an array which may lead to denial of service or potential escalation of privileges.

NVIDIA Tegra kernel contains a vulnerability in the CORE DVFS Thermal driver where there is the potential to read or write a buffer using an index or pointer that references a memory location after the end of the buffer, which may lead to a denial of service or possible escalation of privileges.

NVIDIA ADSP Firmware contains a vulnerability in the ADSP Loader component where there is the potential to write to a memory location that is outside the intended boundary of the buffer, which may lead to denial of service or possible escalation of privileges.

NVIDIA GeForce Experience contains a vulnerability in NVIDIA Web Helper.exe, where untrusted script execution may lead to violation of application execution policy and local code execution.

NVIDIA Vibrante Linux version 1.1, 2.0, and 2.2 contains a vulnerability in the user space driver in which protection mechanisms are insufficient, may lead to denial of service or information disclosure.

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.

Bootloader contains a vulnerability in NVIDIA MB2 where a potential heap overflow might lead to denial of service or escalation of privileges.

Bootloader contains a vulnerability in NVIDIA MB2 where a potential heap overflow could cause memory corruption, which might lead to denial of service or code execution.

Trusty contains a vulnerability in the HDCP service TA where bounds checking in command 10 is missing. The length of an I/O buffer parameter is not checked, which might lead to memory corruption.

Trusty contains a vulnerability in the HDCP service TA where bounds checking in command 9 is missing. Improper restriction of operations within the bounds of a memory buffer might lead to escalation of privileges, information disclosure, and denial of service.

Trusty contains a vulnerability in the HDCP service TA where bounds checking in command 11 is missing. Improper restriction of operations within the bounds of a memory buffer might lead to information disclosure, denial of service, or escalation of privileges.

Bootloader contains a vulnerability in NVIDIA TegraBoot where a potential heap overflow might allow an attacker to control all the RAM after the heap block, leading to denial of service or code execution.

NVIDIA vGPU manager contains a vulnerability in the vGPU plugin, in which an input index is not validated, which may lead to integer overflow, which in turn may cause tampering of data, information disclosure, or denial of service. This affects vGPU version 8.x (prior to 8.6) and version 11.0 (prior to 11.3).

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it doesn't release some resources during driver unload requests from guests. This flaw allows a malicious guest to perform operations by reusing those resources, which may lead to information disclosure, data tampering, or denial of service. This affects vGPU version 12.x (prior to 12.3), version 11.x (prior to 11.5) and version 8.x (prior 8.8).

NVIDIA GPU Display Driver for Windows contains a vulnerability in nvidia-smi where an uncontrolled DLL loading path may lead to arbitrary code execution, denial of service, information disclosure, and data tampering.

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it improperly validates the length field in a request from a guest. This flaw allows a malicious guest to send a length field that is inconsistent with the actual length of the input, which may lead to information disclosure, data tampering, or denial of service. This affects vGPU version 12.x (prior to 12.3), version 11.x (prior to 11.5) and version 8.x (prior 8.8).

NVIDIA GPU Display Driver for Windows, all versions, contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape in which improper access control may lead to denial of service and information disclosure.

NVIDIA Linux kernel distributions contain a vulnerability in FuSa Capture (VI/ISP), where integer underflow due to lack of input validation may lead to complete denial of service, partial integrity, and serious confidentiality loss for all processes in the system.

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where there is the potential to execute privileged operations by the guest OS, which may lead to information disclosure, data tampering, escalation of privileges, and denial of service

NVIDIA vGPU software contains a vulnerability in the guest kernel mode driver and Virtual GPU Manager (vGPU plugin), in which an input length is not validated, which may lead to information disclosure, tampering of data, or denial of service. This affects vGPU version 12.x (prior to 12.2) and version 11.x (prior to 11.4).

NVIDIA Windows GPU Display Driver, all versions, contains a vulnerability in the NVIDIA Control Panel component in which an attacker with local system access can corrupt a system file, which may lead to denial of service or escalation of privileges.

NVIDIA Windows GPU Display Driver, all versions, contains a vulnerability in the kernel mode layer (nvlddmkm.sys) in which the program accesses or uses a pointer that has not been initialized, which may lead to denial of service.

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. 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 operations of type `tf.raw_ops.MatrixDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09. 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.RaggedTensorToVariant`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L129) has an incomplete validation of the splits values, missing the case when the argument would be empty. We have patched the issue in GitHub commit be7a4de6adfbd303ce08be4332554dff70362612. 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.RaggedTensorToSparse`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/ragged_tensor_to_sparse_kernel.cc#L30) has an incomplete validation of the splits values: it does not check that they are in increasing order. We have patched the issue in GitHub commit 1071f554dbd09f7e101324d366eec5f4fe5a3ece. 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. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty 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.

Uninitialized pointer in BIOS firmware for Intel(R) Server Board Families S2600CW, S2600KP, S2600TP, and S2600WT may allow a privileged user to potentially enable escalation of privilege via local access.

In the AIBinder_Class constructor of ibinder.cpp, there is a possible arbitrary code execution due to uninitialized data. This could lead to local escalation of privilege if a process were using libbinder_ndk in a vulnerable way with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-10Android ID: A-161812320

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can generate undefined behavior via a reference binding to nullptr in `BoostedTreesCalculateBestGainsPerFeature` and similar attack can occur in `BoostedTreesCalculateBestFeatureSplitV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. We have patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7. 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.

Memory failure in content protection module due to not having pointer within the scope in Snapdragon Auto, Snapdragon Compute, Snapdragon Mobile, Snapdragon Wired Infrastructure and Networking in Kamorta, QCS404, Rennell, SC7180, SDX55, SM6150, SM7150, SM8250, SXR2130

In Omron CX-Supervisor Versions 3.30 and prior, access of uninitialized pointer vulnerabilities can be exploited when CX Supervisor indirectly calls an initialized pointer when parsing malformed packets.

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.