In the pcfGetProperties function in bitmap/pcfread.c in libXfont through 1.5.2 and 2.x before 2.0.2, a missing boundary check (for PCF files) could be used by local attackers authenticated to an Xserver for a buffer over-read, for information disclosure or a crash of the X server.

A vulnerability was found in libXpm due to a boundary condition within the XpmCreateXpmImageFromBuffer() function. This flaw allows a local attacker to trigger an out-of-bounds read error and read the contents of memory on the system.

A vulnerability was found in libX11 due to a boundary condition within the _XkbReadKeySyms() function. This flaw allows a local user to trigger an out-of-bounds read error and read the contents of memory on the system.

Multiple integer overflows in X.org libXi before 1.7.7 allow remote X servers to cause a denial of service (out-of-bounds memory access or infinite loop) via vectors involving length fields.

The (1) XvQueryAdaptors and (2) XvQueryEncodings functions in X.org libXv before 1.0.11 allow remote X servers to trigger out-of-bounds memory access operations via vectors involving length specifications in received data.

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.

A vulnerability was found in X.Org. This security flaw occurs because the handler for the XIChangeProperty request has a length-validation issues, resulting in out-of-bounds memory reads and potential information disclosure. This issue can lead to local privileges elevation on systems where the X server is running privileged and remote code execution for ssh X forwarding sessions.

The TCP stack in the Linux kernel through 4.10.6 mishandles the SCM_TIMESTAMPING_OPT_STATS feature, which allows local users to obtain sensitive information from the kernel's internal socket data structures or cause a denial of service (out-of-bounds read) via crafted system calls, related to net/core/skbuff.c and net/socket.c.

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.

u'Buffer over read in boot due to size check ignored before copying GUID attribute from request to response' in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8096AU, APQ8098, MDM8207, MDM9150, MDM9205, MDM9206, MDM9207, MDM9250, MDM9607, MDM9628, MDM9650, MSM8108, MSM8208, MSM8209, MSM8608, MSM8905, MSM8909, MSM8998, QCM4290, QCS405, QCS410, QCS4290, QCS603, QCS605, QCS610, QSM8250, SA415M, SA515M, SA6145P, SA6150P, SA6155, SA6155P, SA8150P, SA8155, SA8155P, SA8195P, SC7180, SC8180X, SC8180X+SDX55, SC8180XP, SDA640, SDA670, SDA845, SDA855, SDM1000, SDM640, SDM670, SDM710, SDM712, SDM830, SDM845, SDM850, SDX24, SDX50M, SDX55, SDX55M, SM4125, SM4250, SM4250P, SM6115, SM6115P, SM6125, SM6150, SM6150P, SM6250, SM6250P, SM6350, SM7125, SM7150, SM7150P, SM7225, SM7250, SM7250P, SM8150, SM8150P, SM8250, SXR1120, SXR1130, SXR2130, SXR2130P, WCD9330

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 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.

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.

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 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 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 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.

Possible out of bound read due to lack of length check of data length for a DIAG event in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music

An issue was discovered in the Linux kernel 3.16 through 5.5.6. set_fdc in drivers/block/floppy.c leads to a wait_til_ready out-of-bounds read because the FDC index is not checked for errors before assigning it, aka CID-2e90ca68b0d2.

An out-of-bounds array read in the apr_time_exp*() functions was fixed in the Apache Portable Runtime 1.6.3 release (CVE-2017-12613). The fix for this issue was not carried forward to the APR 1.7.x branch, and hence version 1.7.0 regressed compared to 1.6.3 and is vulnerable to the same issue.

Possible buffer overflow due to lack of buffer length check during management frame Rx handling in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

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.

Possible buffer over read due to improper buffer allocation for file length passed from user space in Snapdragon Auto, Snapdragon Connectivity, Snapdragon Industrial IOT, Snapdragon Mobile

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. 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.

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.

VMware Tools for Windows update addresses an out of bounds read vulnerability in vm3dmp driver which is installed with vmtools in Windows guest machines. This issue is present in versions 10.2.x and 10.3.x prior to 10.3.10. A local attacker with non-administrative access to a Windows guest with VMware Tools installed may be able to leak kernel information or create a denial of service attack on the same Windows guest machine.

When apr_time_exp*() or apr_os_exp_time*() functions are invoked with an invalid month field value in Apache Portable Runtime APR 1.6.2 and prior, out of bounds memory may be accessed in converting this value to an apr_time_exp_t value, potentially revealing the contents of a different static heap value or resulting in program termination, and may represent an information disclosure or denial of service vulnerability to applications which call these APR functions with unvalidated external input.

VMware Workstation (14.x before 14.1.0 and 12.x) and Horizon View Client (4.x before 4.7.0) contain an out-of-bounds read vulnerability in TPView.dll. On Workstation, this issue in conjunction with other bugs may allow a guest to leak information from host or may allow for a Denial of Service on the Windows OS that runs Workstation. In the case of a Horizon View Client, this issue in conjunction with other bugs may allow a View desktop to leak information from host or may allow for a Denial of Service on the Windows OS that runs the Horizon View Client. Exploitation is only possible if virtual printing has been enabled. This feature is not enabled by default on Workstation but it is enabled by default on Horizon View.

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 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).

Lack of boundary checking of a buffer in libSPenBase library of Samsung Notes prior to Samsung Note version 4.3.02.61 allows OOB read

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.

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.

A component of the HarmonyOS has a Out-of-bounds Read vulnerability. Local attackers may exploit this vulnerability to cause kernel out-of-bounds read.

There is an out-of-bound read vulnerability in Taurus-AL00A 10.0.0.1(C00E1R1P1). A module does not verify the some input. Attackers can exploit this vulnerability by sending malicious input through specific app. This could cause out-of-bound, compromising normal service.

An out-of-bounds read in the vrend_blit_need_swizzle function in vrend_renderer.c in virglrenderer through 0.8.0 allows guest OS users to cause a denial of service via VIRGL_CCMD_BLIT commands.

Buffer over read could occur due to incorrect check of buffer size while flashing emmc devices in Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking