Information disclosure while processing IO control commands.

NXP Kinetis K82 devices have a buffer over-read via a crafted wlength value in a GET Status-Other request during use of USB In-System Programming (ISP) mode. This discloses protected flash memory.

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.

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

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.

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

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.

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.

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.

Information disclosure when the ADSP payload size received in HLOS in response to Audio Stream Manager matrix session is less than this expected size.

A flaw was found in the Netfilter subsystem in the Linux kernel. The sctp_mt_check did not validate the flag_count field. This flaw allows a local privileged (CAP_NET_ADMIN) attacker to trigger an out-of-bounds read, leading to a crash or information disclosure.

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

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 security out-of-bounds read information disclosure vulnerability in Trend Micro Worry-Free Business Security Server could allow a local attacker to send garbage data to a specific named pipe and crash the server. Please note: an attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability.

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.

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.

Out of bound read can happen in Widevine TA while copying data to buffer from user data due to lack of check of buffer length received in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon IoT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables, Snapdragon Wired Infrastructure and Networking