A NULL pointer dereference flaw was found in the btrfs_rm_device function in fs/btrfs/volumes.c in the Linux Kernel, where triggering the bug requires ‘CAP_SYS_ADMIN’. This flaw allows a local attacker to crash the system or leak kernel internal information. The highest threat from this vulnerability is to system availability.

Double free in Intel(R) SGX SDK for Linux before version 2.2 and Intel(R) SGX SDK for Windows before version 2.1 may allow an authenticated user to potentially enable information disclosure or denial of service via local access.

The ptrace_setxregs function in arch/xtensa/kernel/ptrace.c in the Linux kernel before 3.1 does not validate user-space pointers, which allows local users to obtain sensitive information from kernel memory locations via a crafted PTRACE_SETXTREGS request.

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.

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.

An out-of-bounds (OOB) memory read flaw was found in the Qualcomm IPC router protocol in the Linux kernel. A missing sanity check allows a local attacker to gain access to out-of-bounds memory, leading to a system crash or a leak of internal kernel information. The highest threat from this vulnerability is to system availability.

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.

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. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. 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. An attacker can access data outside of bounds of heap allocated array in `tf.raw_ops.UnicodeEncode`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/472c1f12ad9063405737679d4f6bd43094e1d36d/tensorflow/core/kernels/unicode_ops.cc) assumes that the `input_value`/`input_splits` pair specify a valid sparse tensor. 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. Due to lack of validation in `tf.raw_ops.Dequantize`, an attacker can trigger a read from outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/26003593aa94b1742f34dc22ce88a1e17776a67d/tensorflow/core/kernels/dequantize_op.cc#L106-L131) accesses the `min_range` and `max_range` tensors in parallel but fails to check that they have the same shape. 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 before 4.18.6. An information leak in cdrom_ioctl_drive_status in drivers/cdrom/cdrom.c could be used by local attackers to read kernel memory because a cast from unsigned long to int interferes with bounds checking. This is similar to CVE-2018-10940.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can read data outside of bounds of heap allocated buffer in `tf.raw_ops.QuantizeAndDequantizeV3`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/11ff7f80667e6490d7b5174aa6bf5e01886e770f/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L237) does not validate the value of user supplied `axis` attribute before using it to index in the array backing the `input` argument. 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 through 5.11.3. drivers/scsi/scsi_transport_iscsi.c is adversely affected by the ability of an unprivileged user to craft Netlink messages.

An issue was discovered in the Linux kernel through 5.11.3. A kernel pointer leak can be used to determine the address of the iscsi_transport structure. When an iSCSI transport is registered with the iSCSI subsystem, the transport's handle is available to unprivileged users via the sysfs file system, at /sys/class/iscsi_transport/$TRANSPORT_NAME/handle. When read, the show_transport_handle function (in drivers/scsi/scsi_transport_iscsi.c) is called, which leaks the handle. This handle is actually the pointer to an iscsi_transport struct in the kernel module's global variables.

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.

There is a use-after-free vulnerability in the Linux kernel through 5.5.2 in the n_tty_receive_buf_common function in drivers/tty/n_tty.c.

There is a use-after-free vulnerability in the Linux kernel through 5.5.2 in the vgacon_invert_region function in drivers/video/console/vgacon.c.

Linux disk/nic frontends data leaks T[his CNA information record relates to multiple CVEs; the text explains which aspects/vulnerabilities correspond to which CVE.] Linux Block and Network PV device frontends don't zero memory regions before sharing them with the backend (CVE-2022-26365, CVE-2022-33740). Additionally the granularity of the grant table doesn't allow sharing less than a 4K page, leading to unrelated data residing in the same 4K page as data shared with a backend being accessible by such backend (CVE-2022-33741, CVE-2022-33742).

Linux disk/nic frontends data leaks T[his CNA information record relates to multiple CVEs; the text explains which aspects/vulnerabilities correspond to which CVE.] Linux Block and Network PV device frontends don't zero memory regions before sharing them with the backend (CVE-2022-26365, CVE-2022-33740). Additionally the granularity of the grant table doesn't allow sharing less than a 4K page, leading to unrelated data residing in the same 4K page as data shared with a backend being accessible by such backend (CVE-2022-33741, CVE-2022-33742).

Linux disk/nic frontends data leaks T[his CNA information record relates to multiple CVEs; the text explains which aspects/vulnerabilities correspond to which CVE.] Linux Block and Network PV device frontends don't zero memory regions before sharing them with the backend (CVE-2022-26365, CVE-2022-33740). Additionally the granularity of the grant table doesn't allow sharing less than a 4K page, leading to unrelated data residing in the same 4K page as data shared with a backend being accessible by such backend (CVE-2022-33741, CVE-2022-33742).

Improper boundary check in sflvd_rdbuf_bits of libsflvextractor prior to SMR Apr-2022 Release 1 allows attackers to read out of bounds memory.

arch/x86/kvm/emulate.c in the Linux kernel through 4.9.3 allows local users to obtain sensitive information from kernel memory or cause a denial of service (use-after-free) via a crafted application that leverages instruction emulation for fxrstor, fxsave, sgdt, and sidt.

The raw_sendmsg function in the Linux kernel 2.6 before 2.6.13.1 allows local users to cause a denial of service (change hardware state) or read from arbitrary memory via crafted input.

Linux disk/nic frontends data leaks T[his CNA information record relates to multiple CVEs; the text explains which aspects/vulnerabilities correspond to which CVE.] Linux Block and Network PV device frontends don't zero memory regions before sharing them with the backend (CVE-2022-26365, CVE-2022-33740). Additionally the granularity of the grant table doesn't allow sharing less than a 4K page, leading to unrelated data residing in the same 4K page as data shared with a backend being accessible by such backend (CVE-2022-33741, CVE-2022-33742).

Improper use of SMS buffer pointer in Shannon baseband prior to SMR Mar-2022 Release 1 allows OOB read.

A vulnerability was found in the pfkey_register function in net/key/af_key.c in the Linux kernel. This flaw allows a local, unprivileged user to gain access to kernel memory, leading to a system crash or a leak of internal kernel information.

An issue was discovered in the Linux kernel before 5.11.8. kernel/bpf/verifier.c has an off-by-one error (with a resultant integer underflow) affecting out-of-bounds speculation on pointer arithmetic, leading to side-channel attacks that defeat Spectre mitigations and obtain sensitive information from kernel memory, aka CID-10d2bb2e6b1d.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for the `QuantizeAndDequantizeV*` operations can trigger a read outside of bounds of heap allocated array. 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 open source platform for machine learning. In affected versions the shape inference code for `tf.ragged.cross` can trigger a read outside of bounds of heap allocated array. 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 open source platform for machine learning. In affected versions the implementation of `FusedBatchNorm` kernels is vulnerable to a 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 open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. This occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer. The code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference functions for `SparseCountSparseOutput` can trigger a read outside of bounds of heap allocated array. 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 open source platform for machine learning. In affected versions the implementation of `SparseBinCount` is vulnerable to a heap OOB access. This is because of missing validation between the elements of the `values` argument and the shape of the sparse output. 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.

An improper boundary check in audio hal service prior to SMR Feb-2022 Release 1 allows attackers to read invalid memory and it leads to application crash.

A flaw was found in the Linux kernel. An index buffer overflow during Direct IO write leading to the NFS client to crash. In some cases, a reach out of the index after one memory allocation by kmalloc will cause a kernel panic. The highest threat from this vulnerability is to data confidentiality and system availability.

Linux kernel 2.4.10 through 2.4.21-pre4 does not properly handle the O_DIRECT feature, which allows local attackers with write privileges to read portions of previously deleted files, or cause file system corruption.

In the Linux kernel 5.0.0-rc7 (as distributed in ubuntu/linux.git on kernel.ubuntu.com), mounting a crafted f2fs filesystem image and performing some operations can lead to slab-out-of-bounds read access in ttm_put_pages in drivers/gpu/drm/ttm/ttm_page_alloc.c. This is related to the vmwgfx or ttm module.

An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. The Wi-Fi kernel drivers have an out-of-bounds Read. The Samsung IDs are SVE-2019-15692, SVE-2019-15693 (December 2019).

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SparseFillEmptyRows` can be made to trigger a heap OOB access. This occurs whenever the size of `indices` does not match the size of `values`. 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.

A information disclosure vulnerability in the Upstream kernel encrypted-keys. Product: Android. Versions: Android kernel. Android ID: A-70526974.

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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 implementations of the `Minimum` and `Maximum` TFLite operators can be used to read data outside of bounds of heap allocated objects, if any of the two input tensor arguments are empty. This is because the broadcasting implementation(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/reference/maximum_minimum.h#L52-L56) indexes in both tensors with the same index but does not validate that the index is within bounds. 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. An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel. Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. 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.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ef0c008ee84bad91ec6725ddc42091e19a30cf0e/tensorflow/core/kernels/maxpooling_op.cc#L1016-L1017) uses the same value to index in two different arrays but there is no guarantee that the sizes are identical. 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. An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments. Each of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads. 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.

NVIDIA GPU Display Driver for Linux, all versions, contains a vulnerability in the kernel mode layer (nvidia.ko) in which it does not completely honor operating system file system permissions to provide GPU device-level isolation, which may lead to denial of service or information disclosure.

In the Linux kernel through 5.2.14 on the powerpc platform, a local user can read vector registers of other users' processes via an interrupt. To exploit the venerability, a local user starts a transaction (via the hardware transactional memory instruction tbegin) and then accesses vector registers. At some point, the vector registers will be corrupted with the values from a different local Linux process, because MSR_TM_ACTIVE is misused in arch/powerpc/kernel/process.c.

In the Linux kernel through 5.2.14 on the powerpc platform, a local user can read vector registers of other users' processes via a Facility Unavailable exception. To exploit the venerability, a local user starts a transaction (via the hardware transactional memory instruction tbegin) and then accesses vector registers. At some point, the vector registers will be corrupted with the values from a different local Linux process because of a missing arch/powerpc/kernel/process.c check.

There is a use-after-free vulnerability in the Linux kernel through 5.5.2 in the vc_do_resize function in drivers/tty/vt/vt.c.