A validation issue was addressed with improved input sanitization. This issue is fixed in iTunes U 3.8.3. Processing a maliciously crafted URL may lead to arbitrary javascript code execution.

There is a denial of service vulnerability in Huawei products. A module cannot deal with specific messages due to validating inputs insufficiently. Attackers can exploit this vulnerability by sending specific messages to affected module. This can cause denial of service. Affected product versions include: S12700 V200R013C00SPC500, V200R019C00SPC500; S5700 V200R013C00SPC500, V200R019C00SPC500; S6700 V200R013C00SPC500, V200R019C00SPC500; S7700 V200R013C00SPC500, V200R019C00SPC500.

An issue was discovered in tcp_rcv() in nptcp.c in HCC embedded InterNiche 4.0.1. The TCP header processing code doesn't sanitize the value of the IP total length field (header length + data length). With a crafted IP packet, an integer overflow occurs whenever the value of the IP data length is calculated by subtracting the length of the header from the total length of the IP packet.

An issue was discovered in HCC Nichestack 3.0. The code that parses TCP packets relies on an unchecked value of the IP payload size (extracted from the IP header) to compute the length of the TCP payload within the TCP checksum computation function. When the IP payload size is set to be smaller than the size of the IP header, the TCP checksum computation function may read out of bounds (a low-impact write-out-of-bounds is also possible).

In memory management driver, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05403499; Issue ID: ALPS05336713.

In memory management driver, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05403499; Issue ID: ALPS05336706.

In memory management driver, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05403499; Issue ID: ALPS05336702.

In memory management driver, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05403499; Issue ID: ALPS05336700.

Improper input validation vulnerability in Bulletin of Cybozu Garoon 4.10.0 to 5.5.0 allows a remote authenticated attacker to obtain the data of Comment and Space without the viewing privilege.

Improper input validation vulnerability in Attaching Files of Cybozu Garoon 4.0.0 to 5.0.2 allows a remote attacker to alter the data of Attaching Files.

Improper input validation vulnerability in E-mail of Cybozu Garoon 4.0.0 to 5.0.2 allows a remote authenticated to alter the data of E-mail without the appropriate privilege.

Improper input validation vulnerability in E-mail of Cybozu Garoon 4.0.0 to 5.0.2 allows a remote attacker with an administrative privilege to alter the data of E-mail without the appropriate privilege.

Improper input validation vulnerability in User Profile of Cybozu Garoon 4.0.0 to 5.0.2 allows a remote authenticated attacker to alter the data of User Profile without the appropriate privilege.

Improper input validation vulnerability in Workflow of Cybozu Garoon 4.0.0 to 5.0.2 allows a remote authenticated attacker to alter the data of Workflow without the appropriate privilege.

Node.js before 16.6.0, 14.17.4, and 12.22.4 is vulnerable to Remote Code Execution, XSS, Application crashes due to missing input validation of host names returned by Domain Name Servers in Node.js dns library which can lead to output of wrong hostnames (leading to Domain Hijacking) and injection vulnerabilities in applications using the library.

The PowerPlay Web component of Mitel Interaction Recording Multitenancy systems before 6.7 could allow a user (with Administrator rights) to replay a previously recorded conversation of another tenant due to insufficient validation.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, `C.TF_TString_Dealloc` is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). To fix this, the call to set the finalizer function is deferred until `NewTensor` returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written. We have patched the issue in GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, which is the other affected version.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.MapStage`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the `key` input is a valid non-empty tensor. We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. 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 due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. 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 due to incomplete validation in `tf.raw_ops.QuantizeV2`, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/quantize_op.cc#L59) has some validation but does not check that `min_range` and `max_range` both have the same non-zero number of elements. If `axis` is provided (i.e., not `-1`), then validation should check that it is a value in range for the rank of `input` tensor and then the lengths of `min_range` and `max_range` inputs match the `axis` dimension of the `input` tensor. We have patched the issue in GitHub commit 6da6620efad397c85493b8f8667b821403516708. 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.