NVIDIA GPU Display Driver for Windows contains a vulnerability in the user mode layer, where an unprivileged regular user can cause an out-of-bounds read. A successful exploit of this vulnerability might lead to code execution, denial of service, escalation of privileges, information disclosure, and data tampering.

Out-of-bounds read in the Intel(R) Trace Analyzer and Collector software before version 2021.5 may allow an authenticated user to potentially enable escalation of privilege via local access.

Out-of-bounds read in the Intel(R) Media SDK software before version 22.2.2 may allow an authenticated user to potentially enable escalation of privilege via local access.

An out-of-bounds access vulnerability involving netfilter was reported and fixed as: f1082dd31fe4 (netfilter: nf_tables: Reject tables of unsupported family); While creating a new netfilter table, lack of a safeguard against invalid nf_tables family (pf) values within `nf_tables_newtable` function enables an attacker to achieve out-of-bounds access.

NVIDIA GPU Display Driver for Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys), where a local user with basic capabilities can cause an out-of-bounds read, which may lead to code execution, denial of service, escalation of privileges, information disclosure, or data tampering.

A flaw was found in xorg-server. Querying or changing XKB button actions such as moving from a touchpad to a mouse can result in out-of-bounds memory reads and writes. This may allow local privilege escalation or possible remote code execution in cases where X11 forwarding is involved.

Buffer Overflow vulenrability in Ffmpeg v.N113007-g8d24a28d06 allows a local attacker to execute arbitrary code via the libavcodec/jpegxl_parser.c in gen_alias_map.

Memory Corruption in WLAN HOST while fetching TX status information.

In the Linux kernel, the following vulnerability has been resolved: ksmbd: fix potencial out-of-bounds when buffer offset is invalid I found potencial out-of-bounds when buffer offset fields of a few requests is invalid. This patch set the minimum value of buffer offset field to ->Buffer offset to validate buffer length.

In tmu_tz_control of tmu.c, there is a possible out of bounds read due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

in OpenHarmony v4.0.0 and prior versions allow a local attacker arbitrary code execution in pre-installed apps through use after free or cause DOS through NULL pointer dereference.

Memory corruption due to out of bound read while parsing a video file in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

Memory corruption occurs while processing command received from HLOS due to improper length check in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables

Buffer Over-read in GitHub repository bfabiszewski/libmobi prior to 0.11. This vulnerability is capable of arbitrary code execution.

Buffer Over-read in GitHub repository vim/vim prior to 8.2.4974.

Out-of-bounds Read in mrb_obj_is_kind_of in in GitHub repository mruby/mruby prior to 3.2. # Impact: Possible arbitrary code execution if being exploited.

In __f2fs_setxattr in fs/f2fs/xattr.c in the Linux kernel through 5.15.11, there is an out-of-bounds memory access when an inode has an invalid last xattr entry.

An improper input validation in saped_dec in libsaped prior to SMR Nov-2023 Release 1 allows local attackers to cause out-of-bounds read and write.

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.

A local privilege escalation vulnerability was found on polkit's pkexec utility. The pkexec application is a setuid tool designed to allow unprivileged users to run commands as privileged users according predefined policies. The current version of pkexec doesn't handle the calling parameters count correctly and ends trying to execute environment variables as commands. An attacker can leverage this by crafting environment variables in such a way it'll induce pkexec to execute arbitrary code. When successfully executed the attack can cause a local privilege escalation given unprivileged users administrative rights on the target machine.

A crafted NTFS image can trigger an out-of-bounds read, caused by an invalid attribute in ntfs_attr_find_in_attrdef, in NTFS-3G < 2021.8.22.

A crafted NTFS image can cause an out-of-bounds read in ntfs_ie_lookup in NTFS-3G < 2021.8.22.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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 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.

Microsoft Resilient File System (ReFS) Elevation of Privilege Vulnerability

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.

Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability

Memory corruption during the image encoding process.

Windows Common Log File System Driver Elevation of Privilege Vulnerability

Out-of-bounds read in Windows Subsystem for Linux allows an authorized attacker to elevate privileges locally.

Windows Kernel Elevation of Privilege Vulnerability

An integer overflow in Silicon Labs Gecko Bootloader version 4.3.1 and earlier allows unbounded memory access when reading from or writing to storage slots.

Windows Kernel Elevation of Privilege Vulnerability

Windows Kernel Elevation of Privilege Vulnerability

Out of bounds read in some Intel(R) Graphics Drivers before versions 15.45.31.5127 and 15.40.45.5126 may allow an authenticated user to potentially enable escalation of privilege via local access.

Out-of-Bounds Read in Virglrenderer in ChromeOS 16093.57.0 allows a malicious guest VM to achieve arbitrary address access within the crosvm sandboxed process, potentially leading to VM escape via crafted vertex elements data triggering an out-of-bounds read in util_format_description.

Memory corruption while processing buffer initialization, when trusted report for certain report types are generated.