An issue was discovered on LG mobile devices with Android OS 9.0 and 10 software. The Wi-Fi subsystem has incorrect input validation, leading to a crash. The LG ID is LVE-SMP-200022 (October 2020).

In TensorFlow release candidate versions 2.4.0rc*, the general implementation for matching filesystem paths to globbing pattern is vulnerable to an access out of bounds of the array holding the directories. There are multiple invariants and preconditions that are assumed by the parallel implementation of GetMatchingPaths but are not verified by the PRs introducing it (#40861 and #44310). Thus, we are completely rewriting the implementation to fully specify and validate these. This is patched in version 2.4.0. This issue only impacts master branch and the release candidates for TF version 2.4. The final release of the 2.4 release will be patched.

An issue was discovered on LG mobile devices with Android OS 7.2, 8.0, 8.1, 9, and 10 software. A service crash may occur because of incorrect input validation. The LG ID is LVE-SMP-200013 (July 2020).

An issue was discovered on LG mobile devices with Android OS 7.2, 8.0, 8.1, 9, and 10 software. An application crash can occur because of incorrect application-level input validation. The LG ID is LVE-SMP-200018 (July 2020).

In the wifi hotspot service, there is a possible denial of service due to a null pointer dereference. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-110476382

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In Tensorflow before version 2.4.0, an attacker can pass an invalid `axis` value to `tf.quantization.quantize_and_dequantize`. This results in accessing a dimension outside the rank of the input tensor in the C++ kernel implementation. However, dim_size only does a DCHECK to validate the argument and then uses it to access the corresponding element of an array. Since in normal builds, `DCHECK`-like macros are no-ops, this results in segfault and access out of bounds of the array. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.

there is a possible Null Pointer Dereference (modem crash) due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, changing the TensorFlow's `SavedModel` protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using `tensorflow-serving` or other inference-as-a-service installments. Fixed were added in commits f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. The issue is patched in commit adf095206f25471e864a8e63a0f1caef53a0e3a6, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

In dhd_tcpdata_info_get of dhd_ip.c, there is a possible Denial of Service due to a precondition check failure. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

A denial-of-service (DoS) vulnerability exists in google.protobuf.json_format.ParseDict() in Python, where the max_recursion_depth limit can be bypassed when parsing nested google.protobuf.Any messages. Due to missing recursion depth accounting inside the internal Any-handling logic, an attacker can supply deeply nested Any structures that bypass the intended recursion limit, eventually exhausting Python’s recursion stack and causing a RecursionError.

In Modem IMS, there is a possible improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In Modem IMS, there is a possible improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In IMS, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In nr modem, there is a possible improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In Modem IMS, there is a possible improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

An exploitable denial-of-service vulnerability exists in the Weave error reporting functionality of the Nest Cam IQ Indoor, version 4620002. A specially crafted weave packets can cause an arbitrary Weave Exchange Session to close, resulting in a denial of service. An attacker can send a specially crafted packet to trigger this vulnerability.

There exists a Denial of service vulnerability in Tink-cc in versions prior to 2.1.3.  * An adversary can crash binaries using the crypto::tink::JsonKeysetReader in tink-cc by providing an input that is not an encoded JSON object, but still a valid encoded JSON element, for example a number or an array. This will crash as Tink just assumes any valid JSON input will contain an object. * An adversary can crash binaries using the crypto::tink::JsonKeysetReader in tink-cc by providing an input containing many nested JSON objects. This may result in a stack overflow. We recommend upgrading to version 2.1.3 or above

An issue was discovered TensorFlow v2.18.0. A Denial of Service (DoS) occurs when padding is set to 'valid' in tf.keras.layers.Conv2D.

In handleRun of TextLine.java, there is a possible application crash due to improper input validation. This could lead to remote denial of service when processing Unicode with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-8.0 Android-8.1 Android-9 Android-10Android ID: A-140632678

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed

An issue was discovered on Samsung mobile devices with N(7.x) and O(8.x) (Broadcom Wi-Fi, and SEC Wi-Fi chipsets) software. Wi-Fi allows a denial of service via TCP SYN packets. The Samsung ID is SVE-2018-13162 (March 2019).

An issue was discovered on Samsung mobile devices with N(7.x), O(8.0), and P(9.0) (Qualcomm chipsets) software. The ESECOMM Trustlet has a NULL pointer dereference. The Samsung ID is SVE-2019-13950 (May 2019).

An issue was discovered on Samsung mobile devices with N(7.x), O(8.0), and P(9.0) (Qualcomm chipsets) software. The Authnr Trustlet has a NULL pointer dereference. The Samsung ID is SVE-2019-13949 (May 2019).

In nr modem, there is a possible system crash due to improper input validation. This could lead to remote denial of service with no additional execution privileges needed.

In parseUriInternal of Intent.java, there is a possible infinite loop 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.

In DeregAcceptProcINT of cn_NrmmStateDeregInit.cpp, there is a possible denial of service due to a logic error in the code. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

Remote prevention of access to cellular service with no user interaction (for example, crashing the cellular radio service with a malformed packet)

In ss_SendCallBarringPwdRequiredIndMsg of ss_CallBarring.c, there is a possible null pointer deref due to a missing null check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an open source platform for machine learning. If `FractionMaxPoolGrad` is given outsize inputs `row_pooling_sequence` and `col_pooling_sequence`, TensorFlow will crash. We have patched the issue in GitHub commit d71090c3e5ca325bdf4b02eb236cfb3ee823e927. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

In Wi-Fi driver, there is a possible way to disconnect Wi-Fi due to an improper resource release. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07030600; Issue ID: ALPS07030600.

TensorFlow is an open source platform for machine learning. The `AvgPoolOp` function takes an argument `ksize` that must be positive but is not checked. A negative `ksize` can trigger a `CHECK` failure and crash the program. We have patched the issue in GitHub commit 3a6ac52664c6c095aa2b114e742b0aa17fdce78f. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds to this issue.

The package com.google.code.gson:gson before 2.8.9 are vulnerable to Deserialization of Untrusted Data via the writeReplace() method in internal classes, which may lead to DoS attacks.

Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then `maxRank` is 0, so we build an empty `SmallVector`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.

Tensorflow is an Open Source Machine Learning Framework. The `GraphDef` format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a `GraphDef` containing a fragment such as the following can be consumed when loading a `SavedModel`. This would result in a stack overflow during execution as resolving each `NodeDef` means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

In wlan, there is a possible denial of service due to incorrect error handling. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS08861558; Issue ID: MSV-1526.

In Telephony, there is a possible out of bounds read due to a missing bounds check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS09289881; Issue ID: MSV-2025.

In Telephony, there is a possible out of bounds read due to a missing bounds check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS09289881; Issue ID: MSV-2024.

In Telephony, there is a possible out of bounds read due to a missing bounds check. This could lead to remote denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS09289881; Issue ID: MSV-2023.