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.
NVIDIA vGPU manager contains a vulnerability in the vGPU plugin, in which an input offset is not validated, which may lead to a buffer overread, 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).
Bootloader contains a vulnerability in the NV3P server where any user with physical access through USB can trigger an incorrect bounds check, which may lead to buffer overflow, resulting in limited information disclosure, limited data integrity, and denial of service across all components.
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability in the Python backend, where an attacker could cause an out-of-bounds read by sending a request. A successful exploit of this vulnerability might lead to information disclosure.
NVIDIA Windows GPU Display Driver, all versions, contains a vulnerability in the DirectX 11 user mode driver (nvwgf2um/x.dll), in which a specially crafted shader can cause an out of bounds access, leading to denial of service.
NVIDIA CUDA Toolkit, all versions prior to 11.1.1, contains a vulnerability in the NVJPEG library in which an out-of-bounds read or write operation may lead to code execution, denial of service, or information disclosure.
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 GPU Display Driver for Linux contains a vulnerability in the kernel mode layer (nvidia.ko), where an integer overflow may lead to information disclosure or data tampering.
NVIDIA Windows GPU Display driver software for Windows (all versions) contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DeviceIoControl where the software reads from a buffer using buffer access mechanisms such as indexes or pointers that reference memory locations after the targeted buffer, which may lead to denial of service.
NVIDIA GPU Display Driver for Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape, where a local user with basic capabilities can cause an out-of-bounds read, which may lead to a system crash or a leak of internal kernel information.
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where an attacker could cause an integer overflow through specially crafted inputs. A successful exploit of this vulnerability might lead to denial of service and data tampering.
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause an integer overflow or wraparound, leading to a segmentation fault, by providing an invalid request. A successful exploit of this vulnerability might lead to denial of service.
NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability where an attacker could read invalid memory. A successful exploit of this vulnerability might lead to information disclosure.
NVIDIA Triton Inference Server for Windows and Linux contains a vulnerability where a user could cause an integer overflow or wraparound, leading to a segmentation fault, by providing an invalid request. A successful exploit of this vulnerability might lead to denial of service.
Clara Genomics Analysis before 0.2.0 has an integer overflow for cudapoa memory management in allocate_block.cpp.
NVIDIA Triton Inference Server contains a vulnerability in the model loading API, where a user could cause an integer overflow or wraparound error by loading a model with an extra-large file size that overflows an internal variable. A successful exploit of this vulnerability might lead to denial of service.
NVIDIA CUDA toolkit for all platforms contains a vulnerability in the cuobjdump binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to cuobjdump. A successful exploit of this vulnerability might lead to a partial denial of service.
NVIDIA CUDA toolkit for all platforms contains a vulnerability in the cuobjdump binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to cuobjdump. A successful exploit of this vulnerability might lead to a partial denial of service.
NVIDIA CUDA toolkit for all platforms contains a vulnerability in the nvdisasm binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to nvdisasm. A successful exploit of this vulnerability might lead to a partial denial of service.
NVIDIA CUDA toolkit for all platforms contains a vulnerability in the nvdisasm binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to nvdisasm. A successful exploit of this vulnerability might lead to a partial denial of service.
NVIDIA CUDA toolkit for all platforms contains a vulnerability in the cuobjdump binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to cuobjdump. A successful exploit of this vulnerability might lead to a partial denial of service.
NVIDIA CUDA toolkit for Windows contains a vulnerability in the cuobjdump binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to cuobjdump. A successful exploit of this vulnerability might lead to a partial denial of service.
NVIDIA DGX A100 SBIOS contains a vulnerability where a local attacker can cause input validation checks to be bypassed by causing an integer overflow. A successful exploit of this vulnerability may lead to denial of service, information disclosure, and data tampering.
NVIDIA Windows GPU Display Driver contains a vulnerability in the kernel mode layer handler for DxgkDdiEscape where the software uses a sequential operation to read or write a buffer, but it uses an incorrect length value that causes it to access memory that is outside of the bounds of the buffer which may lead to denial of service or possible escalation of privileges.
NVIDIA TrustZone Software contains a vulnerability in the Keymaster implementation where the software reads data past the end, or before the beginning, of the intended buffer; and may lead to denial of service or information disclosure. This issue is rated as high.
Trusty contains a vulnerability in the NVIDIA TLK kernel function where a lack of checks allows the exploitation of an integer overflow through a specific SMC call that is triggered by the user, which may lead to denial of service.
NVIDIA CUDA toolkit for all platforms contains a vulnerability in the cuobjdump binary, where a user could cause an out-of-bounds read by passing a malformed ELF file to cuobjdump. A successful exploit of this vulnerability might lead to a partial denial of service.
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).
An out-of-bound read vulnerability in mapToBuffer function in libSDKRecognitionText.spensdk.samsung.so library prior to SMR JAN-2023 Release 1 allows attacker to cause memory access fault.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a `CHECK`-fail in debug builds of TensorFlow using `tf.raw_ops.ResourceGather` or a read from outside the bounds of heap allocated data in the same API in a release build. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L660-L668) does not check that the `batch_dims` value that the user supplies is less than the rank of the input tensor. Since the implementation uses several for loops over the dimensions of `tensor`, this results in reading data from outside the bounds of heap allocated buffer backing the tensor. We have patched the issue in GitHub commit bc9c546ce7015c57c2f15c168b3d9201de679a1d. 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 if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. 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 faceid service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed
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.
Invalid JPEG XL images using libjxl can cause an out of bounds access on a std::vector<std::vector<T>> when rendering splines. The OOB read access can either lead to a segfault, or rendering splines based on other process memory. It is recommended to upgrade past 0.6.0 or patch with https://github.com/libjxl/libjxl/pull/757
Improper input validation in DSP driver prior to SMR Apr-2022 Release 1 allows out-of-bounds write by integer overflow.
A flaw was found in the X Record extension. The RecordSanityCheckRegisterClients function does not check for an integer overflow when computing request length, which allows a client to bypass length checks.
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 affected versions of TensorFlow under certain cases, loading a saved model can result in accessing uninitialized memory while building the computation graph. The MakeEdge function creates an edge between one output tensor of the src node (given by output_index) and the input slot of the dst node (given by input_index). This is only possible if the types of the tensors on both sides coincide, so the function begins by obtaining the corresponding DataType values and comparing these for equality. However, there is no check that the indices point to inside of the arrays they index into. Thus, this can result in accessing data out of bounds of the corresponding heap allocated arrays. In most scenarios, this can manifest as unitialized data access, but if the index points far away from the boundaries of the arrays this can be used to leak addresses from the library. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.
Integer overflow vulnerability during glTF model loading in the 3D engine module Impact: Successful exploitation of this vulnerability may affect availability.
TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. 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 read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) needs to validate that each value in `stats_summary_indices` is in range. We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378. 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 of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. 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.