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 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 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 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.

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

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.

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.

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 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 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.

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.

A flaw was found in dpdk in versions before 18.11.10 and before 19.11.5. A complete lack of validation of attacker-controlled parameters can lead to a buffer over read. The results of the over read are then written back to the guest virtual machine memory. This vulnerability can be used by an attacker in a virtual machine to read significant amounts of host memory. The highest threat from this vulnerability is to data confidentiality and system availability.

NVIDIA GPU Display Driver for Windows and Linux contains a vulnerability in the kernel mode layer, where an unprivileged regular user can cause an out-of-bounds read, which may lead to denial of service and information disclosure.

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.

In all android releases (Android for MSM, Firefox OS for MSM, QRD Android) from CAF using the linux kernel, Venus HW searches for start code when decoding input bit stream buffers. If start code is not found in entire buffer, there is over-fetch beyond allocation length. This leads to page fault.

Out-of-bounds memory access can occur while calculating alignment requirements for a negative width from external components in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music

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.

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.

Possible out of bounds read due to incorrect validation of incoming buffer length in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile

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

Possible buffer over read due to lack of data length check in QVR Service configuration in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Wearables

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.

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.

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

VMware ESXi (6.7 before ESXi670-202006401-SG and 6.5 before ESXi650-202005401-SG), Workstation (15.x before 15.5.5), and Fusion (11.x before 11.5.5) contain an out-of-bounds read vulnerability in NVMe functionality. A malicious actor with local non-administrative access to a virtual machine with a virtual NVMe controller present may be able to read privileged information contained in physical memory.

VMware Workstation (15.x) and Horizon Client for Windows (5.x before 5.4.4) contain an out-of-bounds read vulnerability in Cortado ThinPrint component (JPEG2000 parser). A malicious actor with normal access to a virtual machine may be able to exploit these issues to create a partial denial-of-service condition or to leak memory from TPView process running on the system where Workstation or Horizon Client for Windows is installed.

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

Buffer Over-read at parse_rawml.c:1416 in GitHub repository bfabiszewski/libmobi prior to 0.11. The bug causes the program reads data past the end of the intented buffer. Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash.

Out-of-bounds Read in GitHub repository radareorg/radare2 prior to 5.7.0. The bug causes the program reads data past the end of the intented buffer. Typically, this can allow attackers to read sensitive information from other memory locations or cause a crash.