NVIDIA vGPU software for Windows and Linux contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it allows a guest to access global resources. A successful exploit of this vulnerability might lead to denial of service.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where a local user with basic capabilities can cause a null-pointer dereference, 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 private IOCTLs, where an attacker with local unprivileged system access may cause a NULL pointer dereference, which may lead to denial of service in a component beyond the vulnerable component.

NVIDIA GPU Display Driver for Windows and Linux, all versions, contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape or IOCTL in which improper validation of a user pointer may lead to denial of service.

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (nvidia.ko), where a user in the guest OS can cause a GPU interrupt storm on the hypervisor host, leading to a denial of service.

NVIDIA GPU Display Driver for Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for private IOCTLs where a NULL pointer dereference in the kernel, created within user mode code, may lead to a denial of service in the form of a system crash.

NVIDIA Jetson Linux Driver Package contains a vulnerability in nvbootctrl, where a privileged local attacker can configure invalid settings, resulting in denial of service.

NVIDIA DGX A100 SBIOS contains a vulnerability where a user may cause a dynamic variable evaluation by local access. A successful exploit of this vulnerability may lead to denial of service.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where a NULL-pointer dereference may lead to denial of service.

NVIDIA vGPU software for Windows and Linux contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where a NULL-pointer dereference may lead to denial of service.

NVIDIA GPU Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where an unprivileged regular user can cause a NULL-pointer dereference, which may lead to denial of service.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handlers for all control calls with embedded parameters where dereferencing an untrusted pointer may lead to denial of service.

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it can dereference a NULL pointer, which may lead to denial of service.

NVIDIA Windows GPU Display Driver for Windows contains a vulnerability in the NVIDIA kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape where dereferencing a NULL pointer may lead to a system crash.

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it can dereference a NULL pointer, which may lead to 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 vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it can deadlock, which may lead to denial of service.

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager kernel driver, where a vGPU can cause resource starvation among other vGPUs hosted on the same GPU, which may lead to denial of service.

NVIDIA vGPU software contains a vulnerability in the Virtual GPU Manager kernel mode driver (nvidia.ko), in which a pointer to a user-space buffer is not validated before it is dereferenced, which may lead to 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 vGPU software contains a vulnerability in the Virtual GPU Manager (vGPU plugin), where it can lead to floating point exceptions, which may lead to 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).

Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape, where an attacker through specific configuration and with local unprivileged system access may cause improper input validation, which may lead to denial of service.

NVIDIA vGPU graphics driver for guest OS contains a vulnerability in which an incorrect resource clean up on a failure path can impact the guest VM, leading to denial of service.

NVIDIA Virtual GPU Manager contains a vulnerability in the vGPU plugin, in which it can dereference a NULL pointer, which may lead to denial of service. This affects vGPU version 8.x (prior to 8.5), version 10.x (prior to 10.4) and version 11.0.

In the Linux kernel, the following vulnerability has been resolved: binfmt_elf_fdpic: fix AUXV size calculation when ELF_HWCAP2 is defined create_elf_fdpic_tables() does not correctly account the space for the AUX vector when an architecture has ELF_HWCAP2 defined. Prior to the commit 10e29251be0e ("binfmt_elf_fdpic: fix /proc/<pid>/auxv") it resulted in the last entry of the AUX vector being set to zero, but with that change it results in a kernel BUG. Fix that by adding one to the number of AUXV entries (nitems) when ELF_HWCAP2 is defined.

In the Linux kernel, the following vulnerability has been resolved: drm/i915/gem: Fix Virtual Memory mapping boundaries calculation Calculating the size of the mapped area as the lesser value between the requested size and the actual size does not consider the partial mapping offset. This can cause page fault access. Fix the calculation of the starting and ending addresses, the total size is now deduced from the difference between the end and start addresses. Additionally, the calculations have been rewritten in a clearer and more understandable form. [Joonas: Add Requires: tag] Requires: 60a2066c5005 ("drm/i915/gem: Adjust vma offset for framebuffer mmap offset") (cherry picked from commit 97b6784753da06d9d40232328efc5c5367e53417)

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements. If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`. 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 the Linux kernel, the following vulnerability has been resolved: drm/i915/dsc: Fix the macro that calculates DSCC_/DSCA_ PPS reg address Commit bd077259d0a9 ("drm/i915/vdsc: Add function to read any PPS register") defines a new macro to calculate the DSC PPS register addresses with PPS number as an input. This macro correctly calculates the addresses till PPS 11 since the addresses increment by 4. So in that case the following macro works correctly to give correct register address: _MMIO(_DSCA_PPS_0 + (pps) * 4) However after PPS 11, the register address for PPS 12 increments by 12 because of RC Buffer memory allocation in between. Because of this discontinuity in the address space, the macro calculates wrong addresses for PPS 12 - 16 resulting into incorrect DSC PPS parameter value read/writes causing DSC corruption. This fixes it by correcting this macro to add the offset of 12 for PPS >=12. v3: Add correct paranthesis for pps argument (Jani Nikula) (cherry picked from commit 6074be620c31dc2ae11af96a1a5ea95580976fb5)

In the Linux kernel, the following vulnerability has been resolved: l2tp: pass correct message length to ip6_append_data l2tp_ip6_sendmsg needs to avoid accounting for the transport header twice when splicing more data into an already partially-occupied skbuff. To manage this, we check whether the skbuff contains data using skb_queue_empty when deciding how much data to append using ip6_append_data. However, the code which performed the calculation was incorrect: ulen = len + skb_queue_empty(&sk->sk_write_queue) ? transhdrlen : 0; ...due to C operator precedence, this ends up setting ulen to transhdrlen for messages with a non-zero length, which results in corrupted packets on the wire. Add parentheses to correct the calculation in line with the original intent.

In the Linux kernel, the following vulnerability has been resolved: ASoC: Intel: sof-nau8825: fix module alias overflow The maximum name length for a platform_device_id entry is 20 characters including the trailing NUL byte. The sof_nau8825.c file exceeds that, which causes an obscure error message: sound/soc/intel/boards/snd-soc-sof_nau8825.mod.c:35:45: error: illegal character encoding in string literal [-Werror,-Winvalid-source-encoding] MODULE_ALIAS("platform:adl_max98373_nau8825<U+0018><AA>"); ^~~~ include/linux/module.h:168:49: note: expanded from macro 'MODULE_ALIAS' ^~~~~~ include/linux/module.h:165:56: note: expanded from macro 'MODULE_INFO' ^~~~ include/linux/moduleparam.h:26:47: note: expanded from macro '__MODULE_INFO' = __MODULE_INFO_PREFIX __stringify(tag) "=" info I could not figure out how to make the module handling robust enough to handle this better, but as a quick fix, using slightly shorter names that are still unique avoids the build issue.

An out-of-bounds memory access flaw was found in the Linux kernel Intel’s iSMT SMBus host controller driver in the way a user triggers the I2C_SMBUS_BLOCK_DATA (with the ioctl I2C_SMBUS) with malicious input data. This flaw allows a local user to crash the system.

TensorFlow is an end-to-end open source platform for machine learning. Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., `std::vector<absl::flat_hash_map<int64,T>>`(https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.

A flaw was found in the Linux kernel before 5.9-rc4. A failure of the file system metadata validator in XFS can cause an inode with a valid, user-creatable extended attribute to be flagged as corrupt. This can lead to the filesystem being shutdown, or otherwise rendered inaccessible until it is remounted, leading to a denial of service. The highest threat from this vulnerability is to system availability.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. 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.

An issue was discovered in the Linux kernel 5.9.x through 5.11.3, as used with Xen. In some less-common configurations, an x86 PV guest OS user can crash a Dom0 or driver domain via a large amount of I/O activity. The issue relates to misuse of guest physical addresses when a configuration has CONFIG_XEN_UNPOPULATED_ALLOC but not CONFIG_XEN_BALLOON_MEMORY_HOTPLUG.