In the PatternMatch function in fontfile/fontdir.c in libXfont through 1.5.2 and 2.x before 2.0.2, an attacker with access to an X connection can cause a buffer over-read during pattern matching of fonts, leading to information disclosure or a crash (denial of service). This occurs because '\0' characters are incorrectly skipped in situations involving ? characters.

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.

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.

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.

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 (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.

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.

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.

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.

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 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 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 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 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.

An issue was discovered in certain Apple products. macOS before 10.13.2 is affected. The issue involves the "Intel Graphics Driver" component. It allows local users to bypass intended memory-read restrictions or cause a denial of service (out-of-bounds read and system crash).

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 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

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

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/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. 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. 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 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. 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.

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

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.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape where an out of bounds array access may lead to denial of service or information disclosure.

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).

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 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.

When attempting to create a new XFRM policy, a stack out-of-bounds read will occur if the user provides a template where the mode is set to a value that does not resolve to a valid XFRM mode in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8053, APQ8096AU, APQ8098, IPQ4019, IPQ8074, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8905, MSM8909W, MSM8917, MSM8953, MSM8996AU, QCA4531, QCN7605, QCS605, QM215, SA415M, SC8180X, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM845, SDX20, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR2130

Out of bounds read can happen in diag event set mask command handler when user provided length in the command request is less than expected length in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8096, APQ8096AU, APQ8098, Kamorta, MDM9150, MDM9205, MDM9206, MDM9607, MDM9625, MDM9635M, MDM9640, MDM9650, MDM9655, MSM8905, MSM8909, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996, MSM8996AU, MSM8998, Nicobar, QCM2150, QCN7605, QCS404, QCS405, QCS605, QM215, Rennell, SA415M, Saipan, SC7180, SC8180X, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SXR1130

Lack of boundary checks for data offsets received from HLOS can lead to out-of-bound read in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking in APQ8009, APQ8016, APQ8017, APQ8053, APQ8076, APQ8096, APQ8096AU, APQ8098, MDM9150, MDM9205, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MDM9655, MSM8905, MSM8909, MSM8909W, MSM8917, MSM8920, MSM8937, MSM8940, MSM8953, MSM8996, MSM8996AU, MSM8998, QCM2150, QCS605, QM215, Rennell, SC7180, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM450, SDM630, SDM632, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SM6150, SM7150, SM8150, SXR1130, SXR2130

Out of bound access in diag services when DCI command buffer reallocation is not done properly with required capacity in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Wearables in APQ8009, APQ8096AU, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, QCS605, Rennell, SC8180X, SDM429W, SDM710, SDX55, SM7150, SM8150

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.

Buffer over-read in ADSP parse function due to lack of check for availability of sufficient data payload received in command response in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables in APQ8009, APQ8053, APQ8098, MDM9206, MDM9207C, MDM9607, MDM9640, MDM9650, MSM8905, MSM8909W, MSM8917, MSM8953, QCS605, SDA660, SDA845, SDM429, SDM429W, SDM439, SDM670, SDM710, SDM845, SDX20, SDX24

In Android releases from CAF using the linux kernel (Android for MSM, Firefox OS for MSM, QRD Android) before security patch level 2018-06-05, kernel panic may happen due to out-of-bound read, caused by not checking source buffer length against length of packet stream to be copied.

Out of bound read in in fingerprint application due to requested data assigned to a local buffer without length check in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in Kamorta, MDM9205, Nicobar, QCS404, QCS405, QCS605, Rennell, SA415M, SA6155P, SC7180, SC8180X, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130

An out-of-bounds read was addressed with improved input validation. This issue affected versions prior to macOS Mojave 10.14.2.

Out of bound read in Fingerprint application due to requested data is being used without length check in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wired Infrastructure and Networking in Kamorta, MDM9150, MDM9205, MDM9650, MSM8998, Nicobar, QCS404, QCS405, QCS605, Rennell, SA415M, SA6155P, SC7180, SC8180X, SDA660, SDM630, SDM636, SDM660, SDM670, SDM710, SDM845, SDM850, SDX24, SDX55, SM6150, SM7150, SM8150, SM8250, SXR1130, SXR2130